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Artificial Intelligence and Data Science

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Data Science and Artificial Intelligence for (Better) Science

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Data science and Artificial Intelligence (AI) are changing the way we do science. AI is increasingly used in scientific practices, from data discovery to data analysis, for extracting new knowledge out of research artifacts, generating novel and experimentally testable scientific hypotheses, writing, ...

Keywords : Data Science, Artificial Intelligence, Open Data, research life cycle, knowledge production

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Five Key Trends in AI and Data Science for 2024

These developing issues should be on every leader’s radar screen, data executives say..

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Carolyn Geason-Beissel/MIT SMR | Getty Images

Artificial intelligence and data science became front-page news in 2023. The rise of generative AI, of course, drove this dramatic surge in visibility. So, what might happen in the field in 2024 that will keep it on the front page? And how will these trends really affect businesses?

During the past several months, we’ve conducted three surveys of data and technology executives. Two involved MIT’s Chief Data Officer and Information Quality Symposium attendees — one sponsored by Amazon Web Services (AWS) and another by Thoughtworks . The third survey was conducted by Wavestone , formerly NewVantage Partners, whose annual surveys we’ve written about in the past . In total, the new surveys involved more than 500 senior executives, perhaps with some overlap in participation.

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Surveys don’t predict the future, but they do suggest what those people closest to companies’ data science and AI strategies and projects are thinking and doing. According to those data executives, here are the top five developing issues that deserve your close attention:

1. Generative AI sparkles but needs to deliver value.

As we noted, generative AI has captured a massive amount of business and consumer attention. But is it really delivering economic value to the organizations that adopt it? The survey results suggest that although excitement about the technology is very high , value has largely not yet been delivered. Large percentages of respondents believe that generative AI has the potential to be transformational; 80% of respondents to the AWS survey said they believe it will transform their organizations, and 64% in the Wavestone survey said it is the most transformational technology in a generation. A large majority of survey takers are also increasing investment in the technology. However, most companies are still just experimenting, either at the individual or departmental level. Only 6% of companies in the AWS survey had any production application of generative AI, and only 5% in the Wavestone survey had any production deployment at scale.

Surveys suggest that though excitement about generative AI is very high, value has largely not yet been delivered.

Production deployments of generative AI will, of course, require more investment and organizational change, not just experiments. Business processes will need to be redesigned, and employees will need to be reskilled (or, probably in only a few cases, replaced by generative AI systems). The new AI capabilities will need to be integrated into the existing technology infrastructure.

Perhaps the most important change will involve data — curating unstructured content, improving data quality, and integrating diverse sources. In the AWS survey, 93% of respondents agreed that data strategy is critical to getting value from generative AI, but 57% had made no changes to their data thus far.

2. Data science is shifting from artisanal to industrial.

Companies feel the need to accelerate the production of data science models . What was once an artisanal activity is becoming more industrialized. Companies are investing in platforms, processes and methodologies, feature stores, machine learning operations (MLOps) systems, and other tools to increase productivity and deployment rates. MLOps systems monitor the status of machine learning models and detect whether they are still predicting accurately. If they’re not, the models might need to be retrained with new data.

Producing data models — once an artisanal activity — is becoming more industrialized.

Most of these capabilities come from external vendors, but some organizations are now developing their own platforms. Although automation (including automated machine learning tools, which we discuss below) is helping to increase productivity and enable broader data science participation, the greatest boon to data science productivity is probably the reuse of existing data sets, features or variables, and even entire models.

3. Two versions of data products will dominate.

In the Thoughtworks survey, 80% of data and technology leaders said that their organizations were using or considering the use of data products and data product management. By data product , we mean packaging data, analytics, and AI in a software product offering, for internal or external customers. It’s managed from conception to deployment (and ongoing improvement) by data product managers. Examples of data products include recommendation systems that guide customers on what products to buy next and pricing optimization systems for sales teams.

But organizations view data products in two different ways. Just under half (48%) of respondents said that they include analytics and AI capabilities in the concept of data products. Some 30% view analytics and AI as separate from data products and presumably reserve that term for reusable data assets alone. Just 16% say they don’t think of analytics and AI in a product context at all.

We have a slight preference for a definition of data products that includes analytics and AI, since that is the way data is made useful. But all that really matters is that an organization is consistent in how it defines and discusses data products. If an organization prefers a combination of “data products” and “analytics and AI products,” that can work well too, and that definition preserves many of the positive aspects of product management. But without clarity on the definition, organizations could become confused about just what product developers are supposed to deliver.

4. Data scientists will become less sexy.

Data scientists, who have been called “ unicorns ” and the holders of the “ sexiest job of the 21st century ” because of their ability to make all aspects of data science projects successful, have seen their star power recede. A number of changes in data science are producing alternative approaches to managing important pieces of the work. One such change is the proliferation of related roles that can address pieces of the data science problem. This expanding set of professionals includes data engineers to wrangle data, machine learning engineers to scale and integrate the models, translators and connectors to work with business stakeholders, and data product managers to oversee the entire initiative.

Another factor reducing the demand for professional data scientists is the rise of citizen data science , wherein quantitatively savvy businesspeople create models or algorithms themselves. These individuals can use AutoML, or automated machine learning tools, to do much of the heavy lifting. Even more helpful to citizens is the modeling capability available in ChatGPT called Advanced Data Analysis . With a very short prompt and an uploaded data set, it can handle virtually every stage of the model creation process and explain its actions.

Of course, there are still many aspects of data science that do require professional data scientists. Developing entirely new algorithms or interpreting how complex models work, for example, are tasks that haven’t gone away. The role will still be necessary but perhaps not as much as it was previously — and without the same degree of power and shimmer.

5. Data, analytics, and AI leaders are becoming less independent.

This past year, we began to notice that increasing numbers of organizations were cutting back on the proliferation of technology and data “chiefs,” including chief data and analytics officers (and sometimes chief AI officers). That CDO/CDAO role, while becoming more common in companies, has long been characterized by short tenures and confusion about the responsibilities. We’re not seeing the functions performed by data and analytics executives go away; rather, they’re increasingly being subsumed within a broader set of technology, data, and digital transformation functions managed by a “supertech leader” who usually reports to the CEO. Titles for this role include chief information officer, chief information and technology officer, and chief digital and technology officer; real-world examples include Sastry Durvasula at TIAA, Sean McCormack at First Group, and Mojgan Lefebvre at Travelers.

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This evolution in C-suite roles was a primary focus of the Thoughtworks survey, and 87% of respondents (primarily data leaders but some technology executives as well) agreed that people in their organizations are either completely, to a large degree, or somewhat confused about where to turn for data- and technology-oriented services and issues. Many C-level executives said that collaboration with other tech-oriented leaders within their own organizations is relatively low, and 79% agreed that their organization had been hindered in the past by a lack of collaboration.

We believe that in 2024, we’ll see more of these overarching tech leaders who have all the capabilities to create value from the data and technology professionals reporting to them. They’ll still have to emphasize analytics and AI because that’s how organizations make sense of data and create value with it for employees and customers. Most importantly, these leaders will need to be highly business-oriented, able to debate strategy with their senior management colleagues, and able to translate it into systems and insights that make that strategy a reality.

About the Authors

Thomas H. Davenport ( @tdav ) is the President’s Distinguished Professor of Information Technology and Management at Babson College, a fellow of the MIT Initiative on the Digital Economy, and senior adviser to the Deloitte Chief Data and Analytics Officer Program. He is coauthor of All in on AI: How Smart Companies Win Big With Artificial Intelligence (HBR Press, 2023) and Working With AI: Real Stories of Human-Machine Collaboration (MIT Press, 2022). Randy Bean ( @randybeannvp ) is an industry thought leader, author, founder, and CEO and currently serves as innovation fellow, data strategy, for global consultancy Wavestone. He is the author of Fail Fast, Learn Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI (Wiley, 2021).

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Nicolas corzo.

• DOI: 10.17762/ijritcc.v11i9.9328
• Corpus ID: 266992298

Generative Artificial Intelligence and GPT using Deep Learning: A Comprehensive Vision, Applications Trends and Challenges

• Et al. Ruchi Sharma
• Published in International Journal on… 5 November 2023
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Artificial intelligence and machine learning research: towards digital transformation at a global scale

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• Zain Balfagih 1 ,
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Artificial intelligence (AI) is reshaping how we live, learn, and work. Until recently, AI used to be a fanciful concept, more closely associated with science fiction rather than with anything else. However, driven by unprecedented advances in sophisticated information and communication technology (ICT), AI today is synonymous technological progress already attained and the one yet to come in all spheres of our lives (Chui et al. 2018 ; Lytras et al. 2018 , 2019 ).

Considering that Machine Learning (ML) and AI are apt to reach unforeseen levels of accuracy and efficiency, this special issue sought to promote research on AI and ML seen as functions of data-driven innovation and digital transformation. The combination of expanding ICT-driven capabilities and capacities identifiable across our socio-economic systems along with growing consumer expectations vis-a-vis technology and its value-added for our societies, requires multidisciplinary research and research agenda on AI and ML (Lytras et al. 2021 ; Visvizi et al. 2020 ; Chui et al. 2020 ). Such a research agenda should oscilate around the following five defining issues (Fig. 1 ):

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An AI-Driven Digital Transformation in all aspects of human activity/

Integration of diverse data-warehouses to unified ecosystems of AI and ML value-based services

Deployment of robust AI and ML processing capabilities for enhanced decision making and generation of value our of data.

Design of innovative novel AI and ML applications for predictive and analytical capabilities

Design of sophisticated AI and ML-enabled intelligence components with critical social impact

Promotion of the Digital Transformation in all the aspects of human activity including business, healthcare, government, commerce, social intelligence etc.

Such development will also have a critical impact on government, policies, regulations and initiatives aiming to interpret the value of the AI-driven digital transformation to the sustainable economic development of our planet. Additionally the disruptive character of AI and ML technology and research will required further research on business models and management of innovation capabilities.

This special issue is based on submissions invited from the 17th Annual Learning and Technology Conference 2019 that was held at Effat University and open call jointly. Several very good submissions were received. All of them were subjected a rigorous peer review process specific to the Ambient Intelligence and Humanized Computing Journal.

A variety of innovative topics are included in the agenda of the published papers in this special issue including topics such as:

Stock market Prediction using Machine learning

Detection of Apple Diseases and Pests based on Multi-Model LSTM-based Convolutional Neural Networks

ML for Searching

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Surface EMG Signal Classification

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Over a decade ago, Effat University envisioned a timely platform that brings together educators, researchers and tech enthusiasts under one roof and functions as a fount for creativity and innovation. It was a dream that such platform bridges the existing gap and becomes a leading hub for innovators across disciplines to share their knowledge and exchange novel ideas. It was in 2003 that this dream was realized and the first Learning & Technology Conference was held. Up until today, the conference has covered a variety of cutting-edge themes such as Digital Literacy, Cyber Citizenship, Edutainment, Massive Open Online Courses, and many, many others. The conference has also attracted key, prominent figures in the fields of sciences and technology such as Farouq El Baz from NASA, Queen Rania Al-Abdullah of Jordan, and many others who addressed large, eager-to-learn audiences and inspired many with unique stories.

While emerging innovations, such as Artificial Intelligence technologies, are seen today as promising instruments that could pave our way to the future, these were also the focal points around which fruitful discussions have always taken place here at the L&T. The (AI) was selected for this conference due to its great impact. The Saudi government realized this impact of AI and already started actual steps to invest in AI. It is stated in the Kingdome Vision 2030: "In technology, we will increase our investments in, and lead, the digital economy." Dr. Ahmed Al Theneyan, Deputy Minister of Technology, Industry and Digital Capabilities, stated that: "The Government has invested around USD 3 billion in building the infrastructure so that the country is AI-ready and can become a leader in AI use." Vision 2030 programs also promote innovation in technologies. Another great step that our country made is establishing NEOM city (the model smart city).

Effat University realized this ambition and started working to make it a reality by offering academic programs that support the different sectors needed in such projects. For example, the master program in Energy Engineering was launched four years ago to support the energy sector. Also, the bachelor program of Computer Science has tracks in Artificial Intelligence and Cyber Security which was launched in Fall 2020 semester. Additionally, Energy & Technology and Smart Building Research Centers were established to support innovation in the technology and energy sectors. In general, Effat University works effectively in supporting the KSA to achieve its vision in this time of national transformation by graduating skilled citizen in different fields of technology.

The guest editors would like to take this opportunity to thank all the authors for the efforts they put in the preparation of their manuscripts and for their valuable contributions. We wish to express our deepest gratitude to the referees, who provided instrumental and constructive feedback to the authors. We also extend our sincere thanks and appreciation for the organizing team under the leadership of the Chair of L&T 2019 Conference Steering Committee, Dr. Haifa Jamal Al-Lail, University President, for her support and dedication.

Our sincere thanks go to the Editor-in-Chief for his kind help and support.

Chui KT, Lytras MD, Visvizi A (2018) Energy sustainability in smart cities: artificial intelligence, smart monitoring, and optimization of energy consumption. Energies 11(11):2869

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Chui KT, Fung DCL, Lytras MD, Lam TM (2020) Predicting at-risk university students in a virtual learning environment via a machine learning algorithm. Comput Human Behav 107:105584

Lytras MD, Visvizi A, Daniela L, Sarirete A, De Pablos PO (2018) Social networks research for sustainable smart education. Sustainability 10(9):2974

Lytras MD, Visvizi A, Sarirete A (2019) Clustering smart city services: perceptions, expectations, responses. Sustainability 11(6):1669

Lytras MD, Visvizi A, Chopdar PK, Sarirete A, Alhalabi W (2021) Information management in smart cities: turning end users’ views into multi-item scale development, validation, and policy-making recommendations. Int J Inf Manag 56:102146

Visvizi A, Jussila J, Lytras MD, Ijäs M (2020) Tweeting and mining OECD-related microcontent in the post-truth era: A cloud-based app. Comput Human Behav 107:105958

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Sarirete, A., Balfagih, Z., Brahimi, T. et al. Artificial intelligence and machine learning research: towards digital transformation at a global scale. J Ambient Intell Human Comput 13 , 3319–3321 (2022). https://doi.org/10.1007/s12652-021-03168-y

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Abstract: Given that Large Language Models (LLMs) have made significant progress in writing code, can they now be used to autonomously reproduce results from research repositories? Such a capability would be a boon to the research community, helping researchers validate, understand, and extend prior work. To advance towards this goal, we introduce SUPER, the first benchmark designed to evaluate the capability of LLMs in setting up and executing tasks from research repositories. SUPERaims to capture the realistic challenges faced by researchers working with Machine Learning (ML) and Natural Language Processing (NLP) research repositories. Our benchmark comprises three distinct problem sets: 45 end-to-end problems with annotated expert solutions, 152 sub problems derived from the expert set that focus on specific challenges (e.g., configuring a trainer), and 602 automatically generated problems for larger-scale development. We introduce various evaluation measures to assess both task success and progress, utilizing gold solutions when available or approximations otherwise. We show that state-of-the-art approaches struggle to solve these problems with the best model (GPT-4o) solving only 16.3% of the end-to-end set, and 46.1% of the scenarios. This illustrates the challenge of this task, and suggests that SUPER can serve as a valuable resource for the community to make and measure progress.

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Vergara, D.; Lampropoulos, G.; Fernández-Arias, P.; Antón-Sancho, Á. Artificial Intelligence Reinventing Materials Engineering: A Bibliometric Review. Appl. Sci. 2024 , 14 , 8143. https://doi.org/10.3390/app14188143

Vergara D, Lampropoulos G, Fernández-Arias P, Antón-Sancho Á. Artificial Intelligence Reinventing Materials Engineering: A Bibliometric Review. Applied Sciences . 2024; 14(18):8143. https://doi.org/10.3390/app14188143

Vergara, Diego, Georgios Lampropoulos, Pablo Fernández-Arias, and Álvaro Antón-Sancho. 2024. "Artificial Intelligence Reinventing Materials Engineering: A Bibliometric Review" Applied Sciences 14, no. 18: 8143. https://doi.org/10.3390/app14188143

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Artificial intelligence: A powerful paradigm for scientific research

1 Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China

35 University of Chinese Academy of Sciences, Beijing 100049, China

5 Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China

10 Zhongshan Hospital Institute of Clinical Science, Fudan University, Shanghai 200032, China

Changping Huang

18 Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

11 Institute of Physics, Chinese Academy of Sciences, Beijing 100190, China

37 Songshan Lake Materials Laboratory, Dongguan, Guangdong 523808, China

26 Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China

Xingchen Liu

28 Institute of Coal Chemistry, Chinese Academy of Sciences, Taiyuan 030001, China

2 Institute of Software, Chinese Academy of Sciences, Beijing 100190, China

Fengliang Dong

3 National Center for Nanoscience and Technology, Beijing 100190, China

Cheng-Wei Qiu

4 Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117583, Singapore

6 Department of Gynaecology, Obstetrics and Gynaecology Hospital, Fudan University, Shanghai 200011, China

36 Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases, Shanghai 200011, China

7 School of Food Science and Technology, Dalian Polytechnic University, Dalian 116034, China

41 Second Affiliated Hospital School of Medicine, and School of Public Health, Zhejiang University, Hangzhou 310058, China

8 Department of Obstetrics and Gynecology, Peking University Third Hospital, Beijing 100191, China

9 Zhejiang Provincial People’s Hospital, Hangzhou 310014, China

Chenguang Fu

12 School of Materials Science and Engineering, Zhejiang University, Hangzhou 310027, China

Zhigang Yin

13 Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou 350002, China

Ronald Roepman

14 Medical Center, Radboud University, 6500 Nijmegen, the Netherlands

Sabine Dietmann

15 Institute for Informatics, Washington University School of Medicine, St. Louis, MO 63110, USA

Marko Virta

16 Department of Microbiology, University of Helsinki, 00014 Helsinki, Finland

Fredrick Kengara

17 School of Pure and Applied Sciences, Bomet University College, Bomet 20400, Kenya

19 Agriculture College of Shihezi University, Xinjiang 832000, China

Taolan Zhao

20 Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China

21 The Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

38 Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen 518055, China

Jialiang Yang

22 Geneis (Beijing) Co., Ltd, Beijing 100102, China

23 Department of Communication Studies, Hong Kong Baptist University, Hong Kong, China

24 South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China

39 Center of Economic Botany, Core Botanical Gardens, Chinese Academy of Sciences, Guangzhou 510650, China

Zhaofeng Liu

27 Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China

29 Suzhou Institute of Nano-Tech and Nano-Bionics, Chinese Academy of Sciences, Suzhou 215123, China

Xiaohong Liu

30 Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing 400714, China

James P. Lewis

James m. tiedje.

34 Center for Microbial Ecology, Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA

40 Zhejiang Lab, Hangzhou 311121, China

25 Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai 200031, China

31 Department of Computer Science, Aberystwyth University, Aberystwyth, Ceredigion SY23 3FL, UK

Zhipeng Cai

32 Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA

33 Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China

Jiabao Zhang

Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.

Graphical abstract

Public summary

• • “Can machines think?” The goal of artificial intelligence (AI) is to enable machines to mimic human thoughts and behaviors, including learning, reasoning, predicting, and so on.
• • “Can AI do fundamental research?” AI coupled with machine learning techniques is impacting a wide range of fundamental sciences, including mathematics, medical science, physics, etc.
• • “How does AI accelerate fundamental research?” New research and applications are emerging rapidly with the support by AI infrastructure, including data storage, computing power, AI algorithms, and frameworks.

Introduction

“Can machines think?” Alan Turing posed this question in his famous paper “Computing Machinery and Intelligence.” 1 He believes that to answer this question, we need to define what thinking is. However, it is difficult to define thinking clearly, because thinking is a subjective behavior. Turing then introduced an indirect method to verify whether a machine can think, the Turing test, which examines a machine's ability to show intelligence indistinguishable from that of human beings. A machine that succeeds in the test is qualified to be labeled as artificial intelligence (AI).

AI refers to the simulation of human intelligence by a system or a machine. The goal of AI is to develop a machine that can think like humans and mimic human behaviors, including perceiving, reasoning, learning, planning, predicting, and so on. Intelligence is one of the main characteristics that distinguishes human beings from animals. With the interminable occurrence of industrial revolutions, an increasing number of types of machine types continuously replace human labor from all walks of life, and the imminent replacement of human resources by machine intelligence is the next big challenge to be overcome. Numerous scientists are focusing on the field of AI, and this makes the research in the field of AI rich and diverse. AI research fields include search algorithms, knowledge graphs, natural languages processing, expert systems, evolution algorithms, machine learning (ML), deep learning (DL), and so on.

The general framework of AI is illustrated in Figure 1 . The development process of AI includes perceptual intelligence, cognitive intelligence, and decision-making intelligence. Perceptual intelligence means that a machine has the basic abilities of vision, hearing, touch, etc., which are familiar to humans. Cognitive intelligence is a higher-level ability of induction, reasoning and acquisition of knowledge. It is inspired by cognitive science, brain science, and brain-like intelligence to endow machines with thinking logic and cognitive ability similar to human beings. Once a machine has the abilities of perception and cognition, it is often expected to make optimal decisions as human beings, to improve the lives of people, industrial manufacturing, etc. Decision intelligence requires the use of applied data science, social science, decision theory, and managerial science to expand data science, so as to make optimal decisions. To achieve the goal of perceptual intelligence, cognitive intelligence, and decision-making intelligence, the infrastructure layer of AI, supported by data, storage and computing power, ML algorithms, and AI frameworks is required. Then by training models, it is able to learn the internal laws of data for supporting and realizing AI applications. The application layer of AI is becoming more and more extensive, and deeply integrated with fundamental sciences, industrial manufacturing, human life, social governance, and cyberspace, which has a profound impact on our work and lifestyle.

The general framework of AI

History of AI

The beginning of modern AI research can be traced back to John McCarthy, who coined the term “artificial intelligence (AI),” during at a conference at Dartmouth College in 1956. This symbolized the birth of the AI scientific field. Progress in the following years was astonishing. Many scientists and researchers focused on automated reasoning and applied AI for proving of mathematical theorems and solving of algebraic problems. One of the famous examples is Logic Theorist, a computer program written by Allen Newell, Herbert A. Simon, and Cliff Shaw, which proves 38 of the first 52 theorems in “Principia Mathematica” and provides more elegant proofs for some. 2 These successes made many AI pioneers wildly optimistic, and underpinned the belief that fully intelligent machines would be built in the near future. However, they soon realized that there was still a long way to go before the end goals of human-equivalent intelligence in machines could come true. Many nontrivial problems could not be handled by the logic-based programs. Another challenge was the lack of computational resources to compute more and more complicated problems. As a result, organizations and funders stopped supporting these under-delivering AI projects.

AI came back to popularity in the 1980s, as several research institutions and universities invented a type of AI systems that summarizes a series of basic rules from expert knowledge to help non-experts make specific decisions. These systems are “expert systems.” Examples are the XCON designed by Carnegie Mellon University and the MYCIN designed by Stanford University. The expert system derived logic rules from expert knowledge to solve problems in the real world for the first time. The core of AI research during this period is the knowledge that made machines “smarter.” However, the expert system gradually revealed several disadvantages, such as privacy technologies, lack of flexibility, poor versatility, expensive maintenance cost, and so on. At the same time, the Fifth Generation Computer Project, heavily funded by the Japanese government, failed to meet most of its original goals. Once again, the funding for AI research ceased, and AI was at the second lowest point of its life.

In 2006, Geoffrey Hinton and coworkers 3 , 4 made a breakthrough in AI by proposing an approach of building deeper neural networks, as well as a way to avoid gradient vanishing during training. This reignited AI research, and DL algorithms have become one of the most active fields of AI research. DL is a subset of ML based on multiple layers of neural networks with representation learning, 5 while ML is a part of AI that a computer or a program can use to learn and acquire intelligence without human intervention. Thus, “learn” is the keyword of this era of AI research. Big data technologies, and the improvement of computing power have made deriving features and information from massive data samples more efficient. An increasing number of new neural network structures and training methods have been proposed to improve the representative learning ability of DL, and to further expand it into general applications. Current DL algorithms match and exceed human capabilities on specific datasets in the areas of computer vision (CV) and natural language processing (NLP). AI technologies have achieved remarkable successes in all walks of life, and continued to show their value as backbones in scientific research and real-world applications.

Within AI, ML is having a substantial broad effect across many aspects of technology and science: from computer science to geoscience to materials science, from life science to medical science to chemistry to mathematics and to physics, from management science to economics to psychology, and other data-intensive empirical sciences, as ML methods have been developed to analyze high-throughput data to obtain useful insights, categorize, predict, and make evidence-based decisions in novel ways. To train a system by presenting it with examples of desired input-output behavior, could be far easier than to program it manually by predicting the desired response for all potential inputs. The following sections survey eight fundamental sciences, including information science (informatics), mathematics, medical science, materials science, geoscience, life science, physics, and chemistry, which develop or exploit AI techniques to promote the development of sciences and accelerate their applications to benefit human beings, society, and the world.

AI in information science

AI aims to provide the abilities of perception, cognition, and decision-making for machines. At present, new research and applications in information science are emerging at an unprecedented rate, which is inseparable from the support by the AI infrastructure. As shown in Figure 2 , the AI infrastructure layer includes data, storage and computing power, ML algorithms, and the AI framework. The perception layer enables machines have the basic ability of vision, hearing, etc. For instance, CV enables machines to “see” and identify objects, while speech recognition and synthesis helps machines to “hear” and recognize speech elements. The cognitive layer provides higher ability levels of induction, reasoning, and acquiring knowledge with the help of NLP, 6 knowledge graphs, 7 and continual learning. 8 In the decision-making layer, AI is capable of making optimal decisions, such as automatic planning, expert systems, and decision-supporting systems. Numerous applications of AI have had a profound impact on fundamental sciences, industrial manufacturing, human life, social governance, and cyberspace. The following subsections provide an overview of the AI framework, automatic machine learning (AutoML) technology, and several state-of-the-art AI/ML applications in the information field.

The knowledge graph of the AI framework

The AI framework provides basic tools for AI algorithm implementation

In the past 10 years, applications based on AI algorithms have played a significant role in various fields and subjects, on the basis of which the prosperity of the DL framework and platform has been founded. AI frameworks and platforms reduce the requirement of accessing AI technology by integrating the overall process of algorithm development, which enables researchers from different areas to use it across other fields, allowing them to focus on designing the structure of neural networks, thus providing better solutions to problems in their fields. At the beginning of the 21st century, only a few tools, such as MATLAB, OpenNN, and Torch, were capable of describing and developing neural networks. However, these tools were not originally designed for AI models, and thus faced problems, such as complicated user API and lacking GPU support. During this period, using these frameworks demanded professional computer science knowledge and tedious work on model construction. As a solution, early frameworks of DL, such as Caffe, Chainer, and Theano, emerged, allowing users to conveniently construct complex deep neural networks (DNNs), such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and LSTM conveniently, and this significantly reduced the cost of applying AI models. Tech giants then joined the march in researching AI frameworks. 9 Google developed the famous open-source framework, TensorFlow, while Facebook's AI research team released another popular platform, PyTorch, which is based on Torch; Microsoft Research published CNTK, and Amazon announced MXNet. Among them, TensorFlow, also the most representative framework, referred to Theano's declarative programming style, offering a larger space for graph-based optimization, while PyTorch inherited the imperative programming style of Torch, which is intuitive, user friendly, more flexible, and easier to be traced. As modern AI frameworks and platforms are being widely applied, practitioners can now assemble models swiftly and conveniently by adopting various building block sets and languages specifically suitable for given fields. Polished over time, these platforms gradually developed a clearly defined user API, the ability for multi-GPU training and distributed training, as well as a variety of model zoos and tool kits for specific tasks. 10 Looking forward, there are a few trends that may become the mainstream of next-generation framework development. (1) Capability of super-scale model training. With the emergence of models derived from Transformer, such as BERT and GPT-3, the ability of training large models has become an ideal feature of the DL framework. It requires AI frameworks to train effectively under the scale of hundreds or even thousands of devices. (2) Unified API standard. The APIs of many frameworks are generally similar but slightly different at certain points. This leads to some difficulties and unnecessary learning efforts, when the user attempts to shift from one framework to another. The API of some frameworks, such as JAX, has already become compatible with Numpy standard, which is familiar to most practitioners. Therefore, a unified API standard for AI frameworks may gradually come into being in the future. (3) Universal operator optimization. At present, kernels of DL operator are implemented either manually or based on third-party libraries. Most third-party libraries are developed to suit certain hardware platforms, causing large unnecessary spending when models are trained or deployed on different hardware platforms. The development speed of new DL algorithms is usually much faster than the update rate of libraries, which often makes new algorithms to be beyond the range of libraries' support. 11

To improve the implementation speed of AI algorithms, much research focuses on how to use hardware for acceleration. The DianNao family is one of the earliest research innovations on AI hardware accelerators. 12 It includes DianNao, DaDianNao, ShiDianNao, and PuDianNao, which can be used to accelerate the inference speed of neural networks and other ML algorithms. Of these, the best performance of a 64-chip DaDianNao system can achieve a speed up of 450.65× over a GPU, and reduce the energy by 150.31×. Prof. Chen and his team in the Institute of Computing Technology also designed an Instruction Set Architecture for a broad range of neural network accelerators, called Cambricon, which developed into a serial DL accelerator. After Cambricon, many AI-related companies, such as Apple, Google, HUAWEI, etc., developed their own DL accelerators, and AI accelerators became an important research field of AI.

AI for AI—AutoML

AutoML aims to study how to use evolutionary computing, reinforcement learning (RL), and other AI algorithms, to automatically generate specified AI algorithms. Research on the automatic generation of neural networks has existed before the emergence of DL, e.g., neural evolution. 13 The main purpose of neural evolution is to allow neural networks to evolve according to the principle of survival of the fittest in the biological world. Through selection, crossover, mutation, and other evolutionary operators, the individual quality in a population is continuously improved and, finally, the individual with the greatest fitness represents the best neural network. The biological inspiration in this field lies in the evolutionary process of human brain neurons. The human brain has such developed learning and memory functions that it cannot do without the complex neural network system in the brain. The whole neural network system of the human brain benefits from a long evolutionary process rather than gradient descent and back propagation. In the era of DL, the application of AI algorithms to automatically generate DNN has attracted more attention and, gradually, developed into an important direction of AutoML research: neural architecture search. The implementation methods of neural architecture search are usually divided into the RL-based method and the evolutionary algorithm-based method. In the RL-based method, an RNN is used as a controller to generate a neural network structure layer by layer, and then the network is trained, and the accuracy of the verification set is used as the reward signal of the RNN to calculate the strategy gradient. During the iteration, the controller will give the neural network, with higher accuracy, a higher probability value, so as to ensure that the strategy function can output the optimal network structure. 14 The method of neural architecture search through evolution is similar to the neural evolution method, which is based on a population and iterates continuously according to the principle of survival of the fittest, so as to obtain a high-quality neural network. 15 Through the application of neural architecture search technology, the design of neural networks is more efficient and automated, and the accuracy of the network gradually outperforms that of the networks designed by AI experts. For example, Google's SOTA network EfficientNet was realized through the baseline network based on neural architecture search. 16

AI enabling networking design adaptive to complex network conditions

The application of DL in the networking field has received strong interest. Network design often relies on initial network conditions and/or theoretical assumptions to characterize real network environments. However, traditional network modeling and design, regulated by mathematical models, are unlikely to deal with complex scenarios with many imperfect and high dynamic network environments. Integrating DL into network research allows for a better representation of complex network environments. Furthermore, DL could be combined with the Markov decision process and evolve into the deep reinforcement learning (DRL) model, which finds an optimal policy based on the reward function and the states of the system. Taken together, these techniques could be used to make better decisions to guide proper network design, thereby improving the network quality of service and quality of experience. With regard to the aspect of different layers of the network protocol stack, DL/DRL can be adopted for network feature extraction, decision-making, etc. In the physical layer, DL can be used for interference alignment. It can also be used to classify the modulation modes, design efficient network coding 17 and error correction codes, etc. In the data link layer, DL can be used for resource (such as channels) allocation, medium access control, traffic prediction, 18 link quality evaluation, and so on. In the network (routing) layer, routing establishment and routing optimization 19 can help to obtain an optimal routing path. In higher layers (such as the application layer), enhanced data compression and task allocation is used. Besides the above protocol stack, one critical area of using DL is network security. DL can be used to classify the packets into benign/malicious types, and how it can be integrated with other ML schemes, such as unsupervised clustering, to achieve a better anomaly detection effect.

AI enabling more powerful and intelligent nanophotonics

Nanophotonic components have recently revolutionized the field of optics via metamaterials/metasurfaces by enabling the arbitrary manipulation of light-matter interactions with subwavelength meta-atoms or meta-molecules. 20 , 21 , 22 The conventional design of such components involves generally forward modeling, i.e., solving Maxwell's equations based on empirical and intuitive nanostructures to find corresponding optical properties, as well as the inverse design of nanophotonic devices given an on-demand optical response. The trans-dimensional feature of macro-optical components consisting of complex nano-antennas makes the design process very time consuming, computationally expensive, and even numerically prohibitive, such as device size and complexity increase. DL is an efficient and automatic platform, enabling novel efficient approaches to designing nanophotonic devices with high-performance and versatile functions. Here, we present briefly the recent progress of DL-based nanophotonics and its wide-ranging applications. DL was exploited for forward modeling at first using a DNN. 23 The transmission or reflection coefficients can be well predicted after training on huge datasets. To improve the prediction accuracy of DNN in case of small datasets, transfer learning was introduced to migrate knowledge between different physical scenarios, which greatly reduced the relative error. Furthermore, a CNN and an RNN were developed for the prediction of optical properties from arbitrary structures using images. 24 The CNN-RNN combination successfully predicted the absorption spectra from the given input structural images. In inverse design of nanophotonic devices, there are three different paradigms of DL methods, i.e., supervised, unsupervised, and RL. 25 Supervised learning has been utilized to design structural parameters for the pre-defined geometries, such as tandem DNN and bidirectional DNNs. Unsupervised learning methods learn by themselves without a specific target, and thus are more accessible to discovering new and arbitrary patterns 26 in completely new data than supervised learning. A generative adversarial network (GAN)-based approach, combining conditional GANs and Wasserstein GANs, was proposed to design freeform all-dielectric multifunctional metasurfaces. RL, especially double-deep Q-learning, powers up the inverse design of high-performance nanophotonic devices. 27 DL has endowed nanophotonic devices with better performance and more emerging applications. 28 , 29 For instance, an intelligent microwave cloak driven by DL exhibits millisecond and self-adaptive response to an ever-changing incident wave and background. 28 Another example is that a DL-augmented infrared nanoplasmonic metasurface is developed for monitoring dynamics between four major classes of bio-molecules, which could impact the fields of biology, bioanalytics, and pharmacology from fundamental research, to disease diagnostics, to drug development. 29 The potential of DL in the wide arena of nanophotonics has been unfolding. Even end-users without optics and photonics background could exploit the DL as a black box toolkit to design powerful optical devices. Nevertheless, how to interpret/mediate the intermediate DL process and determine the most dominant factors in the search for optimal solutions, are worthy of being investigated in depth. We optimistically envisage that the advancements in DL algorithms and computation/optimization infrastructures would enable us to realize more efficient and reliable training approaches, more complex nanostructures with unprecedented shapes and sizes, and more intelligent and reconfigurable optic/optoelectronic systems.

AI in other fields of information science

We believe that AI has great potential in the following directions:

• • AI-based risk control and management in utilities can prevent costly or hazardous equipment failures by using sensors that detect and send information regarding the machine's health to the manufacturer, predicting possible issues that could occur so as to ensure timely maintenance or automated shutdown.
• • AI could be used to produce simulations of real-world objects, called digital twins. When applied to the field of engineering, digital twins allow engineers and technicians to analyze the performance of an equipment virtually, thus avoiding safety and budget issues associated with traditional testing methods.
• • Combined with AI, intelligent robots are playing an important role in industry and human life. Different from traditional robots working according to the procedures specified by humans, intelligent robots have the ability of perception, recognition, and even automatic planning and decision-making, based on changes in environmental conditions.
• • AI of things (AIoT) or AI-empowered IoT applications. 30 have become a promising development trend. AI can empower the connected IoT devices, embedded in various physical infrastructures, to perceive, recognize, learn, and act. For instance, smart cities constantly collect data regarding quality-of-life factors, such as the status of power supply, public transportation, air pollution, and water use, to manage and optimize systems in cities. Due to these data, especially personal data being collected from informed or uninformed participants, data security, and privacy 31 require protection.

AI in mathematics

Mathematics always plays a crucial and indispensable role in AI. Decades ago, quite a few classical AI-related approaches, such as k-nearest neighbor, 32 support vector machine, 33 and AdaBoost, 34 were proposed and developed after their rigorous mathematical formulations had been established. In recent years, with the rapid development of DL, 35 AI has been gaining more and more attention in the mathematical community. Equipped with the Markov process, minimax optimization, and Bayesian statistics, RL, 36 GANs, 37 and Bayesian learning 38 became the most favorable tools in many AI applications. Nevertheless, there still exist plenty of open problems in mathematics for ML, including the interpretability of neural networks, the optimization problems of parameter estimation, and the generalization ability of learning models. In the rest of this section, we discuss these three questions in turn.

The interpretability of neural networks

From a mathematical perspective, ML usually constructs nonlinear models, with neural networks as a typical case, to approximate certain functions. The well-known Universal Approximation Theorem suggests that, under very mild conditions, any continuous function can be uniformly approximated on compact domains by neural networks, 39 which serves a vital function in the interpretability of neural networks. However, in real applications, ML models seem to admit accurate approximations of many extremely complicated functions, sometimes even black boxes, which are far beyond the scope of continuous functions. To understand the effectiveness of ML models, many researchers have investigated the function spaces that can be well approximated by them, and the corresponding quantitative measures. This issue is closely related to the classical approximation theory, but the approximation scheme is distinct. For example, Bach 40 finds that the random feature model is naturally associated with the corresponding reproducing kernel Hilbert space. In the same way, the Barron space is identified as the natural function space associated with two-layer neural networks, and the approximation error is measured using the Barron norm. 41 The corresponding quantities of residual networks (ResNets) are defined for the flow-induced spaces. For multi-layer networks, the natural function spaces for the purposes of approximation theory are the tree-like function spaces introduced in Wojtowytsch. 42 There are several works revealing the relationship between neural networks and numerical algorithms for solving partial differential equations. For example, He and Xu 43 discovered that CNNs for image classification have a strong connection with multi-grid (MG) methods. In fact, the pooling operation and feature extraction in CNNs correspond directly to restriction operation and iterative smoothers in MG, respectively. Hence, various convolution and pooling operations used in CNNs can be better understood.

The optimization problems of parameter estimation

In general, the optimization problem of estimating parameters of certain DNNs is in practice highly nonconvex and often nonsmooth. Can the global minimizers be expected? What is the landscape of local minimizers? How does one handle the nonsmoothness? All these questions are nontrivial from an optimization perspective. Indeed, numerous works and experiments demonstrate that the optimization for parameter estimation in DL is itself a much nicer problem than once thought; see, e.g., Goodfellow et al. 44 As a consequence, the study on the solution landscape ( Figure 3 ), also known as loss surface of neural networks, is no longer supposed to be inaccessible and can even in turn provide guidance for global optimization. Interested readers can refer to the survey paper (Sun et al. 45 ) for recent progress in this aspect.

Recent studies indicate that nonsmooth activation functions, e.g., rectified linear units, are better than smooth ones in finding sparse solutions. However, the chain rule does not work in the case that the activation functions are nonsmooth, which then makes the widely used stochastic gradient (SG)-based approaches not feasible in theory. Taking approximated gradients at nonsmooth iterates as a remedy ensures that SG-type methods are still in extensive use, but that the numerical evidence has also exposed their limitations. Also, the penalty-based approaches proposed by Cui et al. 46 and Liu et al. 47 provide a new direction to solve the nonsmooth optimization problems efficiently.

The generalization ability of learning models

A small training error does not always lead to a small test error. This gap is caused by the generalization ability of learning models. A key finding in statistical learning theory states that the generalization error is bounded by a quantity that grows with the increase of the model capacity, but shrinks as the number of training examples increases. 48 A common conjecture relating generalization to solution landscape is that flat and wide minima generalize better than sharp ones. Thus, regularization techniques, including the dropout approach, 49 have emerged to force the algorithms to bypass the sharp minima. However, the mechanism behind this has not been fully explored. Recently, some researchers have focused on the ResNet-type architecture, with dropout being inserted after the last convolutional layer of each modular building. They thus managed to explain the stochastic dropout training process and the ensuing dropout regularization effect from the perspective of optimal control. 50

AI in medical science

There is a great trend for AI technology to grow more and more significant in daily operations, including medical fields. With the growing needs of healthcare for patients, hospital needs are evolving from informationization networking to the Internet Hospital and eventually to the Smart Hospital. At the same time, AI tools and hardware performance are also growing rapidly with each passing day. Eventually, common AI algorithms, such as CV, NLP, and data mining, will begin to be embedded in the medical equipment market ( Figure 4 ).

AI doctor based on electronic medical records

For medical history data, it is inevitable to mention Doctor Watson, developed by the Watson platform of IBM, and Modernizing Medicine, which aims to solve oncology, and is now adopted by CVS & Walgreens in the US and various medical organizations in China as well. Doctor Watson takes advantage of the NLP performance of the IBM Watson platform, which already collected vast data of medical history, as well as prior knowledge in the literature for reference. After inputting the patients' case, Doctor Watson searches the medical history reserve and forms an elementary treatment proposal, which will be further ranked by prior knowledge reserves. With the multiple models stored, Doctor Watson gives the final proposal as well as the confidence of the proposal. However, there are still problems for such AI doctors because, 51 as they rely on prior experience from US hospitals, the proposal may not be suitable for other regions with different medical insurance policies. Besides, the knowledge updating of the Watson platform also relies highly on the updating of the knowledge reserve, which still needs manual work.

AI for public health: Outbreak detection and health QR code for COVID-19

AI can be used for public health purposes in many ways. One classical usage is to detect disease outbreaks using search engine query data or social media data, as Google did for prediction of influenza epidemics 52 and the Chinese Academy of Sciences did for modeling the COVID-19 outbreak through multi-source information fusion. 53 After the COVID-19 outbreak, a digital health Quick Response (QR) code system has been developed by China, first to detect potential contact with confirmed COVID-19 cases and, secondly, to indicate the person's health status using mobile big data. 54 Different colors indicate different health status: green means healthy and is OK for daily life, orange means risky and requires quarantine, and red means confirmed COVID-19 patient. It is easy to use for the general public, and has been adopted by many other countries. The health QR code has made great contributions to the worldwide prevention and control of the COVID-19 pandemic.

Biomarker discovery with AI

High-dimensional data, including multi-omics data, patient characteristics, medical laboratory test data, etc., are often used for generating various predictive or prognostic models through DL or statistical modeling methods. For instance, the COVID-19 severity evaluation model was built through ML using proteomic and metabolomic profiling data of sera 55 ; using integrated genetic, clinical, and demographic data, Taliaz et al. built an ML model to predict patient response to antidepressant medications 56 ; prognostic models for multiple cancer types (such as liver cancer, lung cancer, breast cancer, gastric cancer, colorectal cancer, pancreatic cancer, prostate cancer, ovarian cancer, lymphoma, leukemia, sarcoma, melanoma, bladder cancer, renal cancer, thyroid cancer, head and neck cancer, etc.) were constructed through DL or statistical methods, such as least absolute shrinkage and selection operator （LASSO）, combined with Cox proportional hazards regression model using genomic data. 57

Image-based medical AI

Medical image AI is one of the most developed mature areas as there are numerous models for classification, detection, and segmentation tasks in CV. For the clinical area, CV algorithms can also be used for computer-aided diagnosis and treatment with ECG, CT, eye fundus imaging, etc. As human doctors may be tired and prone to make mistakes after viewing hundreds and hundreds of images for diagnosis, AI doctors can outperform a human medical image viewer due to their specialty at repeated work without fatigue. The first medical AI product approved by FDA is IDx-DR, which uses an AI model to make predictions of diabetic retinopathy. The smartphone app SkinVision can accurately detect melanomas. 58 It uses “fractal analysis” to identify moles and their surrounding skin, based on size, diameter, and many other parameters, and to detect abnormal growth trends. AI-ECG of LEPU Medical can automatically detect heart disease with ECG images. Lianying Medical takes advantage of their hardware equipment to produce real-time high-definition image-guided all-round radiotherapy technology, which successfully achieves precise treatment.

Wearable devices for surveillance and early warning

For wearable devices, AliveCor has developed an algorithm to automatically predict the presence of atrial fibrillation, which is an early warning sign of stroke and heart failure. The 23andMe company can also test saliva samples at a small cost, and a customer can be provided with information based on their genes, including who their ancestors were or potential diseases they may be prone to later in life. It provides accurate health management solutions based on individual and family genetic data. In the 20–30 years of the near feature, we believe there are several directions for further research: (1) causal inference for real-time in-hospital risk prediction. Clinical doctors usually acquire reasonable explanations for certain medical decisions, but the current AI models nowadays are usually black box models. The casual inference will help doctors to explain certain AI decisions and even discover novel ground truths. (2) Devices, including wearable instruments for multi-dimensional health monitoring. The multi-modality model is now a trend for AI research. With various devices to collect multi-modality data and a central processor to fuse all these data, the model can monitor the user's overall real-time health condition and give precautions more precisely. (3) Automatic discovery of clinical markers for diseases that are difficult to diagnose. Diseases, such as ALS, are still difficult for clinical doctors to diagnose because they lack any effective general marker. It may be possible for AI to discover common phenomena for these patients and find an effective marker for early diagnosis.

AI-aided drug discovery

Today we have come into the precision medicine era, and the new targeted drugs are the cornerstones for precision therapy. However, over the past decades, it takes an average of over one billion dollars and 10 years to bring a new drug into the market. How to accelerate the drug discovery process, and avoid late-stage failure, are key concerns for all the big and fiercely competitive pharmaceutical companies. The highlighted emerging role of AI, including ML, DL, expert systems, and artificial neural networks (ANNs), has brought new insights and high efficiency into the new drug discovery processes. AI has been adopted in many aspects of drug discovery, including de novo molecule design, structure-based modeling for proteins and ligands, quantitative structure-activity relationship research, and druggable property judgments. DL-based AI appliances demonstrate superior merits in addressing some challenging problems in drug discovery. Of course, prediction of chemical synthesis routes and chemical process optimization are also valuable in accelerating new drug discovery, as well as lowering production costs.

There has been notable progress in the AI-aided new drug discovery in recent years, for both new chemical entity discovery and the relating business area. Based on DNNs, DeepMind built the AlphaFold platform to predict 3D protein structures that outperformed other algorithms. As an illustration of great achievement, AlphaFold successfully and accurately predicted 25 scratch protein structures from a 43 protein panel without using previously built proteins models. Accordingly, AlphaFold won the CASP13 protein-folding competition in December 2018. 59 Based on the GANs and other ML methods, Insilico constructed a modular drug design platform GENTRL system. In September 2019, they reported the discovery of the first de novo active DDR1 kinase inhibitor developed by the GENTRL system. It took the team only 46 days from target selection to get an active drug candidate using in vivo data. 60 Exscientia and Sumitomo Dainippon Pharma developed a new drug candidate, DSP-1181, for the treatment of obsessive-compulsive disorder on the Centaur Chemist AI platform. In January 2020, DSP-1181 started its phase I clinical trials, which means that, from program initiation to phase I study, the comprehensive exploration took less than 12 months. In contrast, comparable drug discovery using traditional methods usually needs 4–5 years with traditional methods.

How AI transforms medical practice: A case study of cervical cancer

As the most common malignant tumor in women, cervical cancer is a disease that has a clear cause and can be prevented, and even treated, if detected early. Conventionally, the screening strategy for cervical cancer mainly adopts the “three-step” model of “cervical cytology-colposcopy-histopathology.” 61 However, limited by the level of testing methods, the efficiency of cervical cancer screening is not high. In addition, owing to the lack of knowledge by doctors in some primary hospitals, patients cannot be provided with the best diagnosis and treatment decisions. In recent years, with the advent of the era of computer science and big data, AI has gradually begun to extend and blend into various fields. In particular, AI has been widely used in a variety of cancers as a new tool for data mining. For cervical cancer, a clinical database with millions of medical records and pathological data has been built, and an AI medical tool set has been developed. 62 Such an AI analysis algorithm supports doctors to access the ability of rapid iterative AI model training. In addition, a prognostic prediction model established by ML and a web-based prognostic result calculator have been developed, which can accurately predict the risk of postoperative recurrence and death in cervical cancer patients, and thereby better guide decision-making in postoperative adjuvant treatment. 63

AI in materials science

As the cornerstone of modern industry, materials have played a crucial role in the design of revolutionary forms of matter, with targeted properties for broad applications in energy, information, biomedicine, construction, transportation, national security, spaceflight, and so forth. Traditional strategies rely on the empirical trial and error experimental approaches as well as the theoretical simulation methods, e.g., density functional theory, thermodynamics, or molecular dynamics, to discover novel materials. 64 These methods often face the challenges of long research cycles, high costs, and low success rates, and thus cannot meet the increasingly growing demands of current materials science. Accelerating the speed of discovery and deployment of advanced materials will therefore be essential in the coming era.

With the rapid development of data processing and powerful algorithms, AI-based methods, such as ML and DL, are emerging with good potentials in the search for and design of new materials prior to actually manufacturing them. 65 , 66 By integrating material property data, such as the constituent element, lattice symmetry, atomic radius, valence, binding energy, electronegativity, magnetism, polarization, energy band, structure-property relation, and functionalities, the machine can be trained to “think” about how to improve material design and even predict the properties of new materials in a cost-effective manner ( Figure 5 ).

AI is expected to power the development of materials science

AI in discovery and design of new materials

Recently, AI techniques have made significant advances in rational design and accelerated discovery of various materials, such as piezoelectric materials with large electrostrains, 67 organic-inorganic perovskites for photovoltaics, 68 molecular emitters for efficient light-emitting diodes, 69 inorganic solid materials for thermoelectrics, 70 and organic electronic materials for renewable-energy applications. 66 , 71 The power of data-driven computing and algorithmic optimization can promote comprehensive applications of simulation and ML (i.e., high-throughput virtual screening, inverse molecular design, Bayesian optimization, and supervised learning, etc.), in material discovery and property prediction in various fields. 72 For instance, using a DL Bayesian framework, the attribute-driven inverse materials design has been demonstrated for efficient and accurate prediction of functional molecular materials, with desired semiconducting properties or redox stability for applications in organic thin-film transistors, organic solar cells, or lithium-ion batteries. 73 It is meaningful to adopt automation tools for quick experimental testing of potential materials and utilize high-performance computing to calculate their bulk, interface, and defect-related properties. 74 The effective convergence of automation, computing, and ML can greatly speed up the discovery of materials. In the future, with the aid of AI techniques, it will be possible to accomplish the design of superconductors, metallic glasses, solder alloys, high-entropy alloys, high-temperature superalloys, thermoelectric materials, two-dimensional materials, magnetocaloric materials, polymeric bio-inspired materials, sensitive composite materials, and topological (electronic and phonon) materials, and so on. In the past decade, topological materials have ignited the research enthusiasm of condensed matter physicists, materials scientists, and chemists, as they exhibit exotic physical properties with potential applications in electronics, thermoelectrics, optics, catalysis, and energy-related fields. From the most recent predictions, more than a quarter of all inorganic materials in nature are topologically nontrivial. The establishment of topological electronic materials databases 75 , 76 , 77 and topological phononic materials databases 78 using high-throughput methods will help to accelerate the screening and experimental discovery of new topological materials for functional applications. It is recognized that large-scale high-quality datasets are required to practice AI. Great efforts have also been expended in building high-quality materials science databases. As one of the top-ranking databases of its kind, the “atomly.net” materials data infrastructure, 79 has calculated the properties of more than 180,000 inorganic compounds, including their equilibrium structures, electron energy bands, dielectric properties, simulated diffraction patterns, elasticity tensors, etc. As such, the atomly.net database has set a solid foundation for extending AI into the area of materials science research. The X-ray diffraction (XRD)-matcher model of atomly.net uses ML to match and classify the experimental XRD to the simulated patterns. Very recently, by using the dataset from atomly.net, an accurate AI model was built to rapidly predict the formation energy of almost any given compound to yield a fairly good predictive ability. 80

AI-powered Materials Genome Initiative

The Materials Genome Initiative (MGI) is a great plan for rational realization of new materials and related functions, and it aims to discover, manufacture, and deploy advanced materials efficiently, cost-effectively, and intelligently. The initiative creates policy, resources, and infrastructure for accelerating materials development at a high level. This is a new paradigm for the discovery and design of next-generation materials, and runs from a view point of fundamental building blocks toward general materials developments, and accelerates materials development through efforts in theory, computation, and experiment, in a highly integrated high-throughput manner. MGI raises an ultimately high goal and high level for materials development and materials science for humans in the future. The spirit of MGI is to design novel materials by using data pools and powerful computation once the requirements or aspirations of functional usages appear. The theory, computation, and algorithm are the primary and substantial factors in the establishment and implementation of MGI. Advances in theories, computations, and experiments in materials science and engineering provide the footstone to not only accelerate the speed at which new materials are realized but to also shorten the time needed to push new products into the market. These AI techniques bring a great promise to the developing MGI. The applications of new technologies, such as ML and DL, directly accelerate materials research and the establishment of MGI. The model construction and application to science and engineering, as well as the data infrastructure, are of central importance. When the AI-powered MGI approaches are coupled with the ongoing autonomy of manufacturing methods, the potential impact to society and the economy in the future is profound. We are now beginning to see that the AI-aided MGI, among other things, integrates experiments, computation, and theory, and facilitates access to materials data, equips the next generation of the materials workforce, and enables a paradigm shift in materials development. Furthermore, the AI-powdered MGI could also design operational procedures and control the equipment to execute experiments, and to further realize autonomous experimentation in future material research.

Advanced functional materials for generation upgrade of AI

The realization and application of AI techniques depend on the computational capability and computer hardware, and this bases physical functionality on the performance of computers or supercomputers. For our current technology, the electric currents or electric carriers for driving electric chips and devices consist of electrons with ordinary characteristics, such as heavy mass and low mobility. All chips and devices emit relatively remarkable heat levels, consuming too much energy and lowering the efficiency of information transmission. Benefiting from the rapid development of modern physics, a series of advanced materials with exotic functional effects have been discovered or designed, including superconductors, quantum anomalous Hall insulators, and topological fermions. In particular, the superconducting state or topologically nontrivial electrons will promote the next-generation AI techniques once the (near) room temperature applications of these states are realized and implanted in integrated circuits. 81 In this case, the central processing units, signal circuits, and power channels will be driven based on the electronic carriers that show massless, energy-diffusionless, ultra-high mobility, or chiral-protection characteristics. The ordinary electrons will be removed from the physical circuits of future-generation chips and devices, leaving superconducting and topological chiral electrons running in future AI chips and supercomputers. The efficiency of transmission, for information and logic computing will be improved on a vast scale and at a very low cost.

AI for materials and materials for AI

The coming decade will continue to witness the development of advanced ML algorithms, newly emerging data-driven AI methodologies, and integrated technologies for facilitating structure design and property prediction, as well as to accelerate the discovery, design, development, and deployment of advanced materials into existing and emerging industrial sectors. At this moment, we are facing challenges in achieving accelerated materials research through the integration of experiment, computation, and theory. The great MGI, proposed for high-level materials research, helps to promote this process, especially when it is assisted by AI techniques. Still, there is a long way to go for the usage of these advanced functional materials in future-generation electric chips and devices to be realized. More materials and functional effects need to be discovered or improved by the developing AI techniques. Meanwhile, it is worth noting that materials are the core components of devices and chips that are used for construction of computers or machines for advanced AI systems. The rapid development of new materials, especially the emergence of flexible, sensitive, and smart materials, is of great importance for a broad range of attractive technologies, such as flexible circuits, stretchable tactile sensors, multifunctional actuators, transistor-based artificial synapses, integrated networks of semiconductor/quantum devices, intelligent robotics, human-machine interactions, simulated muscles, biomimetic prostheses, etc. These promising materials, devices, and integrated technologies will greatly promote the advancement of AI systems toward wide applications in human life. Once the physical circuits are upgraded by advanced functional or smart materials, AI techniques will largely promote the developments and applications of all disciplines.

AI in geoscience

Ai technologies involved in a large range of geoscience fields.

Momentous challenges threatening current society require solutions to problems that belong to geoscience, such as evaluating the effects of climate change, assessing air quality, forecasting the effects of disaster incidences on infrastructure, by calculating the incoming consumption and availability of food, water, and soil resources, and identifying factors that are indicators for potential volcanic eruptions, tsunamis, floods, and earthquakes. 82 , 83 It has become possible, with the emergence of advanced technology products (e.g., deep sea drilling vessels and remote sensing satellites), for enhancements in computational infrastructure that allow for processing large-scale, wide-range simulations of multiple models in geoscience, and internet-based data analysis that facilitates collection, processing, and storage of data in distributed and crowd-sourced environments. 84 The growing availability of massive geoscience data provides unlimited possibilities for AI—which has popularized all aspects of our daily life (e.g., entertainment, transportation, and commerce)—to significantly contribute to geoscience problems of great societal relevance. As geoscience enters the era of massive data, AI, which has been extensively successful in different fields, offers immense opportunities for settling a series of problems in Earth systems. 85 , 86 Accompanied by diversified data, AI-enabled technologies, such as smart sensors, image visualization, and intelligent inversion, are being actively examined in a large range of geoscience fields, such as marine geoscience, rock physics, geology, ecology, seismicity, environment, hydrology, remote sensing, Arc GIS, and planetary science. 87

Multiple challenges in the development of geoscience

There are some traits of geoscience development that restrict the applicability of fundamental algorithms for knowledge discovery: (1) inherent challenges of geoscience processes, (2) limitation of geoscience data collection, and (3) uncertainty in samples and ground truth. 88 , 89 , 90 Amorphous boundaries generally exist in geoscience objects between space and time that are not as well defined as objects in other fields. Geoscience phenomena are also significantly multivariate, obey nonlinear relationships, and exhibit spatiotemporal structure and non-stationary characteristics. Except for the inherent challenges of geoscience observations, the massive data at multiple dimensions of time and space, with different levels of incompleteness, noise, and uncertainties, disturb processes in geoscience. For supervised learning approaches, there are other difficulties owing to the lack of gold standard ground truth and the “small size” of samples (e.g., a small amount of historical data with sufficient observations) in geoscience applications.

Usage of AI technologies as efficient approaches to promote the geoscience processes

Geoscientists continually make every effort to develop better techniques for simulating the present status of the Earth system (e.g., how much greenhouse gases are released into the atmosphere), and the connections between and within its subsystems (e.g., how does the elevated temperature influence the ocean ecosystem). Viewed from the perspective of geoscience, newly emerging approaches, with the aid of AI, are a perfect combination for these issues in the application of geoscience: (1) characterizing objects and events 91 ; (2) estimating geoscience variables from observations 92 ; (3) forecasting geoscience variables according to long-term observations 85 ; (4) exploring geoscience data relationships 93 ; and (5) causal discovery and causal attribution. 94 While characterizing geoscience objects and events using traditional methods are primarily rooted in hand-coded features, algorithms can automatically detect the data by improving the performance with pattern-mining techniques. However, due to spatiotemporal targets with vague boundaries and the related uncertainties, it can be necessary to advance pattern-mining methods that can explain the temporal and spatial characteristics of geoscience data when characterizing different events and objects. To address the non-stationary issue of geoscience data, AI-aided algorithms have been expanded to integrate the holistic results of professional predictors and engender robust estimations of climate variables (e.g., humidity and temperature). Furthermore, forecasting long-term trends of the current situation in the Earth system using AI-enabled technologies can simulate future scenarios and formulate early resource planning and adaptation policies. Mining geoscience data relationships can help us seize vital signs of the Earth system and promote our understanding of geoscience developments. Of great interest is the advancement of AI-decision methodology with uncertain prediction probabilities, engendering vague risks with poorly resolved tails, signifying the most extreme, transient, and rare events formulated by model sets, which supports various cases to improve accuracy and effectiveness.

AI technologies for optimizing the resource management in geoscience

Currently, AI can perform better than humans in some well-defined tasks. For example, AI techniques have been used in urban water resource planning, mainly due to their remarkable capacity for modeling, flexibility, reasoning, and forecasting the water demand and capacity. Design and application of an Adaptive Intelligent Dynamic Water Resource Planning system, the subset of AI for sustainable water resource management in urban regions, largely prompted the optimization of water resource allocation, will finally minimize the operation costs and improve the sustainability of environmental management 95 ( Figure 6 ). Also, meteorology requires collecting tremendous amounts of data on many different variables, such as humidity, altitude, and temperature; however, dealing with such a huge dataset is a big challenge. 96 An AI-based technique is being utilized to analyze shallow-water reef images, recognize the coral color—to track the effects of climate change, and to collect humidity, temperature, and CO 2 data—to grasp the health of our ecological environment. 97 Beyond AI's capabilities for meteorology, it can also play a critical role in decreasing greenhouse gas emissions originating from the electric-power sector. Comprised of production, transportation, allocation, and consumption of electricity, many opportunities exist in the electric-power sector for Al applications, including speeding up the development of new clean energy, enhancing system optimization and management, improving electricity-demand forecasts and distribution, and advancing system monitoring. 98 New materials may even be found, with the auxiliary of AI, for batteries to store energy or materials and absorb CO 2 from the atmosphere. 99 Although traditional fossil fuel operations have been widely used for thousands of years, AI techniques are being used to help explore the development of more potential sustainable energy sources for the development (e.g., fusion technology). 100

Applications of AI in hydraulic resource management

In addition to the adjustment of energy structures due to climate change (a core part of geoscience systems), a second, less-obvious step could also be taken to reduce greenhouse gas emission: using AI to target inefficiencies. A related statistical report by the Lawrence Livermore National Laboratory pointed out that around 68% of energy produced in the US could be better used for purposeful activities, such as electricity generation or transportation, but is instead contributing to environmental burdens. 101 AI is primed to reduce these inefficiencies of current nuclear power plants and fossil fuel operations, as well as improve the efficiency of renewable grid resources. 102 For example, AI can be instrumental in the operation and optimization of solar and wind farms to make these utility-scale renewable-energy systems far more efficient in the production of electricity. 103 AI can also assist in reducing energy losses in electricity transportation and allocation. 104 A distribution system operator in Europe used AI to analyze load, voltage, and network distribution data, to help “operators assess available capacity on the system and plan for future needs.” 105 AI allowed the distribution system operator to employ existing and new resources to make the distribution of energy assets more readily available and flexible. The International Energy Agency has proposed that energy efficiency is core to the reform of energy systems and will play a key role in reducing the growth of global energy demand to one-third of the current level by 2040.

AI as a building block to promote development in geoscience

The Earth’s system is of significant scientific interest, and affects all aspects of life. 106 The challenges, problems, and promising directions provided by AI are definitely not exhaustive, but rather, serve to illustrate that there is great potential for future AI research in this important field. Prosperity, development, and popularization of AI approaches in the geosciences is commonly driven by a posed scientific question, and the best way to succeed is that AI researchers work closely with geoscientists at all stages of research. That is because the geoscientists can better understand which scientific question is important and novel, which sample collection process can reasonably exhibit the inherent strengths, which datasets and parameters can be used to answer that question, and which pre-processing operations are conducted, such as removing seasonal cycles or smoothing. Similarly, AI researchers are better suited to decide which data analysis approaches are appropriate and available for the data, the advantages and disadvantages of these approaches, and what the approaches actually acquire. Interpretability is also an important goal in geoscience because, if we can understand the basic reasoning behind the models, patterns, or relationships extracted from the data, they can be used as building blocks in scientific knowledge discovery. Hence, frequent communication between the researchers avoids long detours and ensures that analysis results are indeed beneficial to both geoscientists and AI researchers.

AI in the life sciences

The developments of AI and the life sciences are intertwined. The ultimate goal of AI is to achieve human-like intelligence, as the human brain is capable of multi-tasking, learning with minimal supervision, and generalizing learned skills, all accomplished with high efficiency and low energy cost. 107

Mutual inspiration between AI and neuroscience

In the past decades, neuroscience concepts have been introduced into ML algorithms and played critical roles in triggering several important advances in AI. For example, the origins of DL methods lie directly in neuroscience, 5 which further stimulated the emergence of the field of RL. 108 The current state-of-the-art CNNs incorporate several hallmarks of neural computation, including nonlinear transduction, divisive normalization, and maximum-based pooling of inputs, 109 which were directly inspired by the unique processing of visual input in the mammalian visual cortex. 110 By introducing the brain's attentional mechanisms, a novel network has been shown to produce enhanced accuracy and computational efficiency at difficult multi-object recognition tasks than conventional CNNs. 111 Other neuroscience findings, including the mechanisms underlying working memory, episodic memory, and neural plasticity, have inspired the development of AI algorithms that address several challenges in deep networks. 108 These algorithms can be directly implemented in the design and refinement of the brain-machine interface and neuroprostheses.

On the other hand, insights from AI research have the potential to offer new perspectives on the basics of intelligence in the brains of humans and other species. Unlike traditional neuroscientists, AI researchers can formalize the concepts of neural mechanisms in a quantitative language to extract their necessity and sufficiency for intelligent behavior. An important illustration of such exchange is the development of the temporal-difference (TD) methods in RL models and the resemblance of TD-form learning in the brain. 112 Therefore, the China Brain Project covers both basic research on cognition and translational research for brain disease and brain-inspired intelligence technology. 113

AI for omics big data analysis

Currently, AI can perform better than humans in some well-defined tasks, such as omics data analysis and smart agriculture. In the big data era, 114 there are many types of data (variety), the volume of data is big, and the generation of data (velocity) is fast. The high variety, big volume, and fast velocity of data makes having it a matter of big value, but also makes it difficult to analyze the data. Unlike traditional statistics-based methods, AI can easily handle big data and reveal hidden associations.

In genetics studies, there are many successful applications of AI. 115 One of the key questions is to determine whether a single amino acid polymorphism is deleterious. 116 There have been sequence conservation-based SIFT 117 and network-based SySAP, 118 but all these methods have met bottlenecks and cannot be further improved. Sundaram et al. developed PrimateAI, which can predict the clinical outcome of mutation based on DNN. 119 Another problem is how to call copy-number variations, which play important roles in various cancers. 120 , 121 Glessner et al. proposed a DL-based tool DeepCNV, in which the area under the receiver operating characteristic (ROC) curve was 0.909, much higher than other ML methods. 122 In epigenetic studies, m6A modification is one of the most important mechanisms. 123 Zhang et al. developed an ensemble DL predictor (EDLm6APred) for mRNA m6A site prediction. 124 The area under the ROC curve of EDLm6APred was 86.6%, higher than existing m6A methylation site prediction models. There are many other DL-based omics tools, such as DeepCpG 125 for methylation, DeepPep 126 for proteomics, AtacWorks 127 for assay for transposase-accessible chromatin with high-throughput sequencing, and deepTCR 128 for T cell receptor sequencing.

Another emerging application is DL for single-cell sequencing data. Unlike bulk data, in which the sample size is usually much smaller than the number of features, the sample size of cells in single-cell data could also be big compared with the number of genes. That makes the DL algorithm applicable for most single-cell data. Since the single-cell data are sparse and have many unmeasured missing values, DeepImpute can accurately impute these missing values in the big gene × cell matrix. 129 During the quality control of single-cell data, it is important to remove the doublet solo embedded cells, using autoencoder, and then build a feedforward neural network to identify the doublet. 130 Potential energy underlying single-cell gradients used generative modeling to learn the underlying differentiation landscape from time series single-cell RNA sequencing data. 131

In protein structure prediction, the DL-based AIphaFold2 can accurately predict the 3D structures of 98.5% of human proteins, and will predict the structures of 130 million proteins of other organisms in the next few months. 132 It is even considered to be the second-largest breakthrough in life sciences after the human genome project 133 and will facilitate drug development among other things.

AI makes modern agriculture smart

Agriculture is entering a fourth revolution, termed agriculture 4.0 or smart agriculture, benefiting from the arrival of the big data era as well as the rapid progress of lots of advanced technologies, in particular ML, modern information, and communication technologies. 134 , 135 Applications of DL, information, and sensing technologies in agriculture cover the whole stages of agricultural production, including breeding, cultivation, and harvesting.

Traditional breeding usually exploits genetic variations by searching natural variation or artificial mutagenesis. However, it is hard for either method to expose the whole mutation spectrum. Using DL models trained on the existing variants, predictions can be made on multiple unidentified gene loci. 136 For example, an ML method, multi-criteria rice reproductive gene predictor, was developed and applied to predict coding and lincRNA genes associated with reproductive processes in rice. 137 Moreover, models trained in species with well-studied genomic data (such as Arabidopsis and rice) can also be applied to other species with limited genome information (such as wild strawberry and soybean). 138 In most cases, the links between genotypes and phenotypes are more complicated than we expected. One gene can usually respond to multiple phenotypes, and one trait is generally the product of the synergism between multi-genes and multi-development. For this reason, multi-traits DL models were developed and enabled genomic editing in plant breeding. 139 , 140

It is well known that dynamic and accurate monitoring of crops during the whole growth period is vitally important to precision agriculture. In the new stage of agriculture, both remote sensing and DL play indispensable roles. Specifically, remote sensing (including proximal sensing) could produce agricultural big data from ground, air-borne, to space-borne platforms, which have a unique potential to offer an economical approach for non-destructive, timely, objective, synoptic, long-term, and multi-scale information for crop monitoring and management, thereby greatly assisting in precision decisions regarding irrigation, nutrients, disease, pests, and yield. 141 , 142 DL makes it possible to simply, efficiently, and accurately discover knowledge from massive and complicated data, especially for remote sensing big data that are characterized with multiple spatial-temporal-spectral information, owing to its strong capability for feature representation and superiority in capturing the essential relation between observation data and agronomy parameters or crop traits. 135 , 143 Integration of DL and big data for agriculture has demonstrated the most disruptive force, as big as the green revolution. As shown in Figure 7 , for possible application a scenario of smart agriculture, multi-source satellite remote sensing data with various geo- and radio-metric information, as well as abundance of spectral information from UV, visible, and shortwave infrared to microwave regions, can be collected. In addition, advanced aircraft systems, such as unmanned aerial vehicles with multi/hyper-spectral cameras on board, and smartphone-based portable devices, will be used to obtain multi/hyper-spectral data in specific fields. All types of data can be integrated by DL-based fusion techniques for different purposes, and then shared for all users for cloud computing. On the cloud computing platform, different agriculture remote sensing models developed by a combination of data-driven ML methods and physical models, will be deployed and applied to acquire a range of biophysical and biochemical parameters of crops, which will be further analyzed by a decision-making and prediction system to obtain the current water/nutrient stress, growth status, and to predict future development. As a result, an automatic or interactive user service platform can be accessible to make the correct decisions for appropriate actions through an integrated irrigation and fertilization system.

Integration of AI and remote sensing in smart agriculture

Furthermore, DL presents unique advantages in specific agricultural applications, such as for dense scenes, that increase the difficulty of artificial planting and harvesting. It is reported that CNNs and Autoencoder models, trained with image data, are being used increasingly for phenotyping and yield estimation, 144 such as counting fruits in orchards, grain recognition and classification, disease diagnosis, etc. 145 , 146 , 147 Consequently, this may greatly liberate the labor force.

The application of DL in agriculture is just beginning. There are still many problems and challenges for the future development of DL technology. We believe, with the continuous acquisition of massive data and the optimization of algorithms, DL will have a better prospect in agricultural production.

AI in physics

The scale of modern physics ranges from the size of a neutron to the size of the Universe ( Figure 8 ). According to the scale, physics can be divided into four categories: particle physics on the scale of neutrons, nuclear physics on the scale of atoms, condensed matter physics on the scale of molecules, and cosmic physics on the scale of the Universe. AI, also called ML, plays an important role in all physics in different scales, since the use of the AI algorithm will be the main trend in data analyses, such as the reconstruction and analysis of images.

Scale of the physics

Speeding up simulations and identifications of particles with AI

There are many applications or explorations of applications of AI in particle physics. We cannot cover all of them here, but only use lattice quantum chromodynamics (LQCD) and the experiments on the Beijing spectrometer (BES) and the large hadron collider (LHC) to illustrate the power of ML in both theoretical and experimental particle physics.

LQCD studies the nonperturbative properties of QCD by using Monte Carlo simulations on supercomputers to help us understand the strong interaction that binds quarks together to form nucleons. Markov chain Monte Carlo simulations commonly used in LQCD suffer from topological freezing and critical slowing down as the simulations approach the real situation of the actual world. New algorithms with the help of DL are being proposed and tested to overcome those difficulties. 148 , 149 Physical observables are extracted from LQCD data, whose signal-to-noise ratio deteriorates exponentially. For non-Abelian gauge theories, such as QCD, complicated contour deformations can be optimized by using ML to reduce the variance of LQCD data. Proof-of-principle applications in two dimensions have been studied. 150 ML can also be used to reduce the time cost of generating LQCD data. 151

On the experimental side, particle identification (PID) plays an important role. Recently, a few PID algorithms on BES-III were developed, and the ANN 152 is one of them. Also, extreme gradient boosting has been used for multi-dimensional distribution reweighting, muon identification, and cluster reconstruction, and can improve the muon identification. U-Net is a convolutional network for pixel-level semantic segmentation, which is widely used in CV. It has been applied on BES-III to solve the problem of multi-turn curling track finding for the main drift chamber. The average efficiency and purity for the first turn's hits is about 91%, at the threshold of 0.85. Current (and future) particle physics experiments are producing a huge amount of data. Machine leaning can be used to discriminate between signal and overwhelming background events. Examples of data analyses on LHC, using supervised ML, can be found in a 2018 collaboration. 153 To take the potential advantage of quantum computers forward, quantum ML methods are also being investigated, see, for example, Wu et al., 154 and references therein, for proof-of-concept studies.

AI makes nuclear physics powerful

Cosmic ray muon tomography (Muography) 155 is an imaging graphe technology using natural cosmic ray muon radiation rather than artificial radiation to reduce the dangers. As an advantage, this technology can detect high-Z materials without destruction, as muon is sensitive to high-Z materials. The Classification Model Algorithm (CMA) algorithm is based on the classification in the supervised learning and gray system theory, and generates a binary classifier designing and decision function with the input of the muon track, and the output indicates whether the material exists at the location. The AI helps the user to improve the efficiency of the scanning time with muons.

AIso, for nuclear detection, the Cs 2 LiYCl 6 :Ce (CLYC) signal can react to both electrons and neutrons to create a pulse signal, and can therefore be applied to detect both neutrons and electrons, 156 but needs identification of the two particles by analyzing the shapes of the waves, that is n-γ ID. The traditional method has been the PSD (pulse shape discrimination) method, which is used to separate the waves of two particles by analyzing the distribution of the pulse information—such as amplitude, width, raise time, fall time, and the two particles that can be separated when the distribution has two separated Gaussian distributions. The traditional PSD can only analyze single-pulse waves, rather than multipulse waves, when two particles react with CLYC closely. But it can be solved by using an ANN method for classification of the six categories (n,γ,n + n,n + γ,γ + n,γ). Also, there are several parameters that could be used by AI to improve the reconstruction algorithm with high efficiency and less error.

AI-aided condensed matter physics

AI opens up a new avenue for physical science, especially when a trove of data is available. Recent works demonstrate that ML provides useful insights to improve the density functional theory (DFT), in which the single-electron picture of the Kohn-Sham scheme has the difficulty of taking care of the exchange and correlation effects of many-body systems. Yu et al. proposed a Bayesian optimization algorithm to fit the Hubbard U parameter, and the new method can find the optimal Hubbard U through a self-consistent process with good efficiency compared with the linear response method, 157 and boost the accuracy to the near-hybrid-functional-level. Snyder et al. developed an ML density functional for a 1D non-interacting non-spin-polarized fermion system to obtain significantly improved kinetic energy. This method enabled a direct approximation of the kinetic energy of a quantum system and can be utilized in orbital-free DFT modeling, and can even bypass the solving of the Kohn-Sham equation—while maintaining the precision to the quantum chemical level when a strong correlation term is included. Recently, FermiNet showed that the many-body quantum mechanics equations can be solved via AI. AI models also show advantages of capturing the interatom force field. In 2010, the Gaussian approximation potential (GAP) 158 was introduced as a powerful interatomic force field to describe the interactions between atoms. GAP uses kernel regression and invariant many-body representations, and performs quite well. For instance, it can simulate crystallization of amorphous crystals under high pressure fairly accurately. By employing the smooth overlap of the atomic position kernel (SOAP), 159 the accuracy of the potential can be further enhanced and, therefore, the SOAP-GAP can be viewed as a field-leading method for AI molecular dynamic simulation. There are also several other well-developed AI interatomic potentials out there, e.g., crystal graph CNNs provide a widely applicable way of vectorizing crystalline materials; SchNet embeds the continuous-filter convolutional layers into its DNNs for easing molecular dynamic as the potentials are space continuous; DimeNet constructs the directional message passing neural network by adding not only the bond length between atoms but also the bond angle, the dihedral angle, and the interactions between unconnected atoms into the model to obtain good accuracy.

AI helps explore the Universe

AI is one of the newest technologies, while astronomy is one of the oldest sciences. When the two meet, new opportunities for scientific breakthroughs are often triggered. Observations and data analysis play a central role in astronomy. The amount of data collected by modern telescopes has reached unprecedented levels, even the most basic task of constructing a catalog has become challenging with traditional source-finding tools. 160 Astronomers have developed automated and intelligent source-finding tools based on DL, which not only offer significant advantages in operational speed but also facilitate a comprehensive understanding of the Universe by identifying particular forms of objects that cannot be detected by traditional software and visual inspection. 160 , 161

More than a decade ago, a citizen science project called “Galaxy Zoo” was proposed to help label one million images of galaxies collected by the Sloan Digital Sky Survey (SDSS) by posting images online and recruiting volunteers. 162 Larger optical telescopes, in operation or under construction, produce data several orders of magnitude higher than SDSS. Even with volunteers involved, there is no way to analyze the vast amount of data received. The advantages of ML are not limited to source-finding and galaxy classification. In fact, it has a much wider range of applications. For example, CNN plays an important role in detecting and decoding gravitational wave signals in real time, reconstructing all parameters within 2 ms, while traditional algorithms take several days to accomplish the same task. 163 Such DL systems have also been used to automatically generate alerts for transients and track asteroids and other fast-moving near-Earth objects, improving detection efficiency by several orders of magnitude. In addition, astrophysicists are exploring the use of neural networks to measure galaxy clusters and study the evolution of the Universe.

In addition to the amazing speed, neural networks seem to have a deeper understanding of the data than expected and can recognize more complex patterns, indicating that the “machine” is evolving rather than just learning the characteristics of the input data.

AI in chemistry

Chemistry plays an important “central” role in other sciences 164 because it is the investigation of the structure and properties of matter, and identifies the chemical reactions that convert substances into to other substances. Accordingly, chemistry is a data-rich branch of science containing complex information resulting from centuries of experiments and, more recently, decades of computational analysis. This vast treasure trove of data is most apparent within the Chemical Abstract Services, which has collected more than 183 million unique organic and inorganic substances, including alloys, coordination compounds, minerals, mixtures, polymers, and salts, and is expanding by addition of thousands of additional new substances daily. 165 The unlimited complexity in the variety of material compounds explains why chemistry research is still a labor-intensive task. The level of complexity and vast amounts of data within chemistry provides a prime opportunity to achieve significant breakthroughs with the application of AI. First, the type of molecules that can be constructed from atoms are almost unlimited, which leads to unlimited chemical space 166 ; the interconnection of these molecules with all possible combinations of factors, such as temperature, substrates, and solvents, are overwhelmingly large, giving rise to unlimited reaction space. 167 Exploration of the unlimited chemical space and reaction space, and navigating to the optimum ones with the desired properties, is thus practically impossible solely from human efforts. Secondly, in chemistry, the huge assortment of molecules and the interplay of them with the external environments brings a new level of complexity, which cannot be simply predicted using physical laws. While many concepts, rules, and theories have been generalized from centuries of experience from studying trivial (i.e., single component) systems, nontrivial complexities are more likely as we discover that “more is different” in the words of Philip Warren Anderson, American physicist and Nobel Laureate. 168 Nontrivial complexities will occur when the scale changes, and the breaking of symmetry in larger, increasingly complex systems, and the rules will shift from quantitative to qualitative. Due to lack of systematic and analytical theory toward the structures, properties, and transformations of macroscopic substances, chemistry research is thus, incorrectly, guided by heuristics and fragmental rules accumulated over the previous centuries, yielding progress that only proceeds through trial and error. ML will recognize patterns from large amounts of data; thereby offering an unprecedented way of dealing with complexity, and reshaping chemistry research by revolutionizing the way in which data are used. Every sub-field of chemistry, currently, has utilized some form of AI, including tools for chemistry research and data generation, such as analytical chemistry and computational chemistry, as well as application to organic chemistry, catalysis, and medical chemistry, which we discuss herein.

AI breaks the limitations of manual feature selection methods

In analytical chemistry, the extraction of information has traditionally relied heavily on the feature selection techniques, which are based on prior human experiences. Unfortunately, this approach is inefficient, incomplete, and often biased. Automated data analysis based on AI will break the limitations of manual variable selection methods by learning from large amounts of data. Feature selection through DL algorithms enables information extraction from the datasets in NMR, chromatography, spectroscopy, and other analytical tools, 169 thereby improving the model prediction accuracy for analysis. These ML approaches will greatly accelerate the analysis of materials, leading to the rapid discovery of new molecules or materials. Raman scattering, for instance, since its discovery in the 1920s, has been widely employed as a powerful vibrational spectroscopy technology, capable of providing vibrational fingerprints intrinsic to analytes, thus enabling identification of molecules. 170 Recently, ML methods have been trained to recognize features in Raman (or SERS) spectra for the identity of an analyte by applying DL networks, including ANN, CNN, and fully convolutional network for feature engineering. 171 For example, Leong et al. designed a machine-learning-driven “SERS taster” to simultaneously harness useful vibrational information from multiple receptors for enhanced multiplex profiling of five wine flavor molecules at ppm levels. Principal-component analysis is employed for the discrimination of alcohols with varying degrees of substitution, and supported with vector machine discriminant analysis, is used to quantitatively classify all flavors with 100% accuracy. 172 Overall, AI techniques provide the first glimmer of hope for a universal method for spectral data analysis, which is fast, accurate, objective and definitive and with attractive advantages in a wide range of applications.

AI improves the accuracy and efficiency for various levels of computational theory

Complementary to analytical tools, computational chemistry has proven a powerful approach for using simulations to understand chemical properties; however, it is faced with an accuracy-versus-efficiency dilemma. This dilemma greatly limits the application of computational chemistry to real-world chemistry problems. To overcome this dilemma, ML and other AI methods are being applied to improve the accuracy and efficiency for various levels of theory used to describe the effects arising at different time and length scales, in the multi-scaling of chemical reactions. 173 Many of the open challenges in computational chemistry can be solved by ML approaches, for example, solving Schrödinger's equation, 174 developing atomistic 175 or coarse graining 176 potentials, constructing reaction coordinates, 177 developing reaction kinetics models, 178 and identifying key descriptors for computable properties. 179 In addition to analytical chemistry and computational chemistry, several disciplines of chemistry have incorporated AI technology to chemical problems. We discuss the areas of organic chemistry, catalysis, and medical chemistry as examples of where ML has made a significant impact. Many examples exist in literature for other subfields of chemistry and AI will continue to demonstrate breakthroughs in a wide range of chemical applications.

AI enables robotics capable of automating the synthesis of molecules

Organic chemistry studies the structure, property, and reaction of carbon-based molecules. The complexity of the chemical and reaction space, for a given property, presents an unlimited number of potential molecules that can be synthesized by chemists. Further complications are added when faced with the problems of how to synthesize a particular molecule, given that the process relies much on heuristics and laborious testing. Challenges have been addressed by researchers using AI. Given enough data, any properties of interest of a molecule can be predicted by mapping the molecular structure to the corresponding property using supervised learning, without resorting to physical laws. In addition to known molecules, new molecules can be designed by sampling the chemical space 180 using methods, such as autoencoders and CNNs, with the molecules coded as sequences or graphs. Retrosynthesis, the planning of synthetic routes, which was once considered an art, has now become much simpler with the help of ML algorithms. The Chemetica system, 181 for instance, is now capable of autonomous planning of synthetic routes that are subsequently proven to work in the laboratory. Once target molecules and the route of synthesis are determined, suitable reaction conditions can be predicted or optimized using ML techniques. 182

The integration of these AI-based approaches with robotics has enabled fully AI-guided robotics capable of automating the synthesis of small organic molecules without human intervention Figure 9 . 183 , 184

A closed loop workflow to enable automatic and intelligent design, synthesis, and assay of molecules in organic chemistry by AI

AI helps to search through vast catalyst design spaces

Catalytic chemistry originates from catalyst technologies in the chemical industry for efficient and sustainable production of chemicals and fuels. Thus far, it is still a challenging endeavor to make novel heterogeneous catalysts with good performance (i.e., stable, active, and selective) because a catalyst's performance depends on many properties: composition, support, surface termination, particle size, particle morphology, atomic coordination environment, porous structure, and reactor during the reaction. The inherent complexity of catalysis makes discovering and developing catalysts with desired properties more dependent on intuition and experiment, which is costly and time consuming. AI technologies, such as ML, when combined with experimental and in silico high-throughput screening of combinatorial catalyst libraries, can aid catalyst discovery by helping to search through vast design spaces. With a well-defined structure and standardized data, including reaction results and in situ characterization results, the complex association between catalytic structure and catalytic performance will be revealed by AI. 185 , 186 An accurate descriptor of the effect of molecules, molecular aggregation states, and molecular transport, on catalysts, could also be predicted. With this approach, researchers can build virtual laboratories to develop new catalysts and catalytic processes.

AI enables screening of chemicals in toxicology with minimum ethical concerns

A more complicated sub-field of chemistry is medical chemistry, which is a challenging field due to the complex interactions between the exotic substances and the inherent chemistry within a living system. Toxicology, for instance, as a broad field, seeks to predict and eliminate substances (e.g., pharmaceuticals, natural products, food products, and environmental substances), which may cause harm to a living organism. Living organisms are already complex, nearly any known substance can cause toxicity at a high enough exposure because of the already inherent complexity within living organisms. Moreover, toxicity is dependent on an array of other factors, including organism size, species, age, sex, genetics, diet, combination with other chemicals, overall health, and/or environmental context. Given the scale and complexity of toxicity problems, AI is likely to be the only realistic approach to meet regulatory body requirements for screening, prioritization, and risk assessment of chemicals (including mixtures), therefore revolutionizing the landscape in toxicology. 187 In summary, AI is turning chemistry from a labor-intensive branch of science to a highly intelligent, standardized, and automated field, and much more can be achieved compared with the limitation of human labor. Underlying knowledge with new concepts, rules, and theories is expected to advance with the application of AI algorithms. A large portion of new chemistry knowledge leading to significant breakthroughs is expected to be generated from AI-based chemistry research in the decades to come.

Conclusions

This paper carries out a comprehensive survey on the development and application of AI across a broad range of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. Despite the fact that AI has been pervasively used in a wide range of applications, there still exist ML security risks on data and ML models as attack targets during both training and execution phases. Firstly, since the performance of an ML system is highly dependent on the data used to train it, these input data are crucial for the security of the ML system. For instance, adversarial example attacks 188 providing malicious input data often lead the ML system into making false judgments (predictions or categorizations) with small perturbations that are imperceptible to humans; data poisoning by intentionally manipulating raw, training, or testing data can result in a decrease in model accuracy or lead to other error-specific attack purposes. Secondly, ML model attacks include backdoor attacks on DL, CNN, and federated learning that manipulate the model's parameters directly, as well as model stealing attack, model inversion attack, and membership inference attack, which can steal the model parameters or leak the sensitive training data. While a number of defense techniques against these security threats have been proposed, new attack models that target ML systems are constantly emerging. Thus, it is necessary to address the problem of ML security and develop robust ML systems that remain effective under malicious attacks.

Due to the data-driven character of the ML method, features of the training and testing data must be drawn from the same distribution, which is difficult to guarantee in practice. This is because, in practical application, the data source might be different from that in the training dataset. In addition, the data feature distribution may drift over time, which leads to a decline of the performance of the model. Moreover, if the model is trained with only new data, it will lead to catastrophic “forgetting” of the model, which means the model only remembers the new features and forgets the previously learned features. To solve this problem, more and more scholars pay attention on how to make the model have the ability of lifelong learning, that is, a change in the computing paradigm from “offline learning + online reasoning” to “online continuous learning,” and thus give the model have the ability of lifelong learning, just like a human being.

Acknowledgments

This work was partially supported by the National Key R&D Program of China (2018YFA0404603, 2019YFA0704900, 2020YFC1807000, and 2020YFB1313700), the Youth Innovation Promotion Association CAS (2011225, 2012006, 2013002, 2015316, 2016275, 2017017, 2017086, 2017120, 2017204, 2017300, 2017399, 2018356, 2020111, 2020179, Y201664, Y201822, and Y201911), NSFC (nos. 11971466, 12075253, 52173241, and 61902376), the Foundation of State Key Laboratory of Particle Detection and Electronics (SKLPDE-ZZ-201902), the Program of Science & Technology Service Network of CAS (KFJ-STS-QYZX-050), the Fundamental Science Center of the National Nature Science Foundation of China (nos. 52088101 and 11971466), the Scientific Instrument Developing Project of CAS (ZDKYYQ20210003), the Strategic Priority Research Program (B) of CAS (XDB33000000), the National Science Foundation of Fujian Province for Distinguished Young Scholars (2019J06023), the Key Research Program of Frontier Sciences, CAS (nos. ZDBS-LY-7022 and ZDBS-LY-DQC012), the CAS Project for Young Scientists in Basic Research (no. YSBR-005). The study is dedicated to the 10th anniversary of the Youth Innovation Promotion Association of the Chinese Academy of Sciences.

Author contributions

Y.X., Q.W., Z.A., Fei W., C.L., Z.C., J.M.T., and J.Z. conceived and designed the research. Z.A., Q.W., Fei W., Libo.Z., Y.W., F.D., and C.W.-Q. wrote the “ AI in information science ” section. Xin.L. wrote the “ AI in mathematics ” section. J.Q., K.H., W.S., J.W., H.X., Y.H., and X.C. wrote the “ AI in medical science ” section. E.L., C.F., Z.Y., and M.L. wrote the “ AI in materials science ” section. Fang W., R.R., S.D., M.V., and F.K. wrote the “ AI in geoscience ” section. C.H., Z.Z., L.Z., T.Z., J.D., J.Y., L.L., M.L., and T.H. wrote the “ AI in life sciences ” section. Z.L., S.Q., and T.A. wrote the “ AI in physics ” section. X.L., B.Z., X.H., S.C., X.L., W.Z., and J.P.L. wrote the “ AI in chemistry ” section. Y.X., Q.W., and Z.A. wrote the “Abstract,” “ introduction ,” “ history of AI ,” and “ conclusions ” sections.

Declaration of interests

The authors declare no competing interests.

Published Online: October 28, 2021

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Active funding opportunity

Nsf 23-614: smart health and biomedical research in the era of artificial intelligence and advanced data science (sch), program solicitation, document information, document history.

• Posted: August 14, 2023
• Replaces: NSF 21-530

Program Solicitation NSF 23-614

Full Proposal Deadline(s) (due by 5 p.m. submitting organization’s local time):

     November 09, 2023

     October 03, 2024

     October 3, Annually Thereafter

Important Information And Revision Notes

• Themes areas have been revised;
• Changes have been made in participating National Institutes of Health’s Institutes and Centers; and,
• Proposal deadlines have been revised.

Any proposal submitted in response to this solicitation should be submitted in accordance with the NSF Proposal & Award Policies & Procedures Guide (PAPPG) that is in effect for the relevant due date to which the proposal is being submitted. The NSF PAPPG is regularly revised and it is the responsibility of the proposer to ensure that the proposal meets the requirements specified in this solicitation and the applicable version of the PAPPG. Submitting a proposal prior to a specified deadline does not negate this requirement.

Summary Of Program Requirements

General information.

Program Title:

Smart Health and Biomedical Research in the Era of Artificial Intelligence and Advanced Data Science (SCH)

The purpose of this interagency program solicitation is to support the development of transformative high-risk, high-reward advances in computer and information science, engineering, mathematics, statistics, behavioral and/or cognitive research to address pressing questions in the biomedical and public health communities. Transformations hinge on scientific and engineering innovations by interdisciplinary teams that develop novel methods to intuitively and intelligently collect, sense, connect, analyze and interpret data from individuals, devices and systems to enable discovery and optimize health. Solutions to these complex biomedical or public health problems demand the formation of interdisciplinary teams that are ready to address these issues, while advancing fundamental science and engineering.

Cognizant Program Officer(s):

Please note that the following information is current at the time of publishing. See program website for any updates to the points of contact.

Goli Yamini, Directorate for Computer and Information Science and Engineering, Division of Information and Intelligent Systems, telephone: 703-292-5367, email: [email protected]

Thomas Martin, Directorate for Computer and Information Science and Engineering, Division of Information and Intelligent Systems, telephone: 703-292-2170, email: [email protected]

Steven J. Breckler, Social, Behavioral and Economic Sciences Directorate, Division of Behavioral and Cognitive Sciences, telephone: 703-292-7369, email: [email protected]

Yulia Gel, Mathematics and Physical Sciences Directorate, Division of Mathematical Sciences, telephone: 703-292-7888, email: [email protected]

Georgia-Ann Klutke, Directorate for Engineering, Division of Civil, Mechanical and Manufacturing Innovation, telephone: 703-292-2443, email: [email protected]

Tatiana Korelsky, Directorate for Computer and Information Science and Engineering, Division of Information and Intelligent Systems, telephone: 703-292-8930, email: [email protected]

Shivani Sharma, Directorate for Engineering, Division of Civil, Mechanical and Manufacturing Innovation, telephone: 703-292-4204, email: [email protected]

Vishal Sharma, Directorate for Computer and Information Science and Engineering, Division of Computer and Network Systems, telephone: 703-292-8950, email: [email protected]

Sylvia Spengler, Directorate for Computer and Information Science and Engineering, Division of Information and Intelligent Systems, telephone: 703-292-8930, email: [email protected]

Betty K. Tuller, Social, Behavioral and Economic Sciences Directorate, Division of Behavioral and Cognitive Sciences, telephone: 703-292-7238, email: [email protected]

Christopher C. Yang, Directorate for Computer and Information Science and Engineering, Division of Information and Intelligent Systems, telephone: 703-292-8111, email: [email protected]

James E. Fowler, Computer and Information Science and Engineering, Computing and Communication Foundations, telephone: 703-292-8910, email: [email protected]

Dana Wolff-Hughes, National Cancer Institute (NCI), telephone: 240-620-0673, email: [email protected]

James Gao, National Eye Institute (NEI), NIH, telephone: 301-594-6074, email: [email protected]

Julia Berzhanskaya, National Heart, Lung, and Blood Institute (NHLBI), NIH, telephone: 301-443-3707, email: [email protected]

Lyndon Joseph, National Institute on Aging (NIA), telephone: 301-496-6761, email: [email protected]

Aron Marquitz, National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS), telephone: 301-435-1240, email: [email protected]

Qi Duan, National Institute of Biomedical Imaging and Bioengineering (NIBIB), NIH, telephone: 301-827-4674, email: [email protected]

Samantha Calabrese, Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), NIH, telephone: 301-827-7568, email: [email protected]

Susan Wright, National Institute on Drug Abuse (NIDA), NIH, telephone: 301-402-6683, email: [email protected]

Roger Miller, National Institute on Deafness and Other Communication Disorders (NIDCD), NIH, telephone: 301-402-3458, email: [email protected]

Noffisat Oki, National Institute of Dental and Craniofacial Research (NIDCR), telephone: 301-402-6778, email: [email protected]

Xujing Wang, National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), NIH, telephone: 301-451-2682, email: [email protected]

Christopher Duncan, National Institute of Environmental Health Sciences (NIEHS), NIH, telephone: 984-287-3256, email: [email protected]

David Leitman, National Institute of Mental Health (NIMH), telephone: 301-827-6131, email: [email protected]

Deborah Duran, National Institute on Minority Health and Health Disparities (NIMHD), NIH, telephone: 301-594-9809, email: [email protected]

Leslie C. Osborne, National Institute of Neurological Disorders and Stroke (NINDS), telephone: 240-921-135, email: [email protected]

Bill Duval, National Institute of Nursing Research (NINR), NIH, telephone: 301-435-0380, email: [email protected]

Goutham Reddy, National Library of Medicine (NLM), telephone: 301-827-6728, email: [email protected]

Emrin Horguslouglu, National Center for Complementary and Integrative Health (NCCIH), telephone: 240-383-5302, email: [email protected]

Christopher Hartshorn, National Center for Advancing Translational Sciences (NCATS), telephone: 301-402-0264, email: [email protected]

Joseph Monaco, Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) Initiative, telephone: 301-402-3823, email: [email protected]

Robert Cregg, Office of AIDS Research (OAR), telephone: 301-761-7557, email: [email protected]

Jacqueline Lloyd, Office of Disease Prevention (ODP), telephone: 301-827-5559, email: [email protected]

Fenglou Mao, Office of Data Science Strategy (ODSS), Division of Program Coordination, Planning, and Strategic Initiatives (DPCPSI), telephone: 301-451-9389, email: [email protected]

Nicholas Jury, Office of Nutrition Research (ONR), telephone: 301-827-1234, email: [email protected]

Jamie White, Office of Research on Women’s Health (ORWH), telephone: 301-496-9200, email: [email protected]

Adam Berger, Office of Science Policy (OSP), telephone: 301-827-9676, email: [email protected]

Christopher Barnhart, Sexual and Gender Minority Research Office (SGMRO), telephone: 301-594-8983, email: [email protected]

• 47.041 --- Engineering
• 47.049 --- Mathematical and Physical Sciences
• 47.070 --- Computer and Information Science and Engineering
• 47.075 --- Social Behavioral and Economic Sciences
• 93.173 --- National Institute on Deafness and Other Communication Disorders
• 93.213 --- National Center for Complementary and Integrative Health
• 93.242 --- National Institute of Mental Health
• 93.279 --- National Institute on Drug Abuse
• 93.286 --- National Institute of Biomedical Imaging and Bioengineering
• 93.310 --- NIH Office of Data Science
• 93.350 --- National Center for Advancing Translational Sciences
• 93.361 --- National Institute of Nursing Research
• 93.396 --- National Cancer Institute
• 93.846 --- National Institute of Arthritis and Musculoskeletal and Skin Disease
• 93.847 --- National Institute of Diabetes and Digestive and Kidney Diseases
• 93.853 --- National Institute of Neurological Disorders and Stroke
• 93.865 --- Eunice Kennedy Shriver National Institute of Child Health and Human Development
• 93.866 --- National Institute on Aging
• 93.867 --- National Eye Institute
• 93.879 --- National Library of Medicine

Award Information

Anticipated Type of Award: Standard Grant or Continuing Grant or Cooperative Agreement

Estimated Number of Awards: 10 to 16

per year, subject to the availability of funds.

Projects will be funded for up to four years for a total of $1,200,000 ($300,000 per year).

Anticipated Funding Amount: $15,000,000 to $20,000,000

will be invested in proposals submitted to this solicitation in each year of the solicitation, subject to the availability of funds and the quality of the proposals received.

Eligibility Information

Who May Submit Proposals:

Proposals may only be submitted by the following: Institutions of Higher Education (IHEs) - Two- and four-year IHEs (including community colleges) accredited in, and having a campus located in the US, acting on behalf of their faculty members. Special Instructions for International Branch Campuses of US IHEs: If the proposal includes funding to be provided to an international branch campus of a US institution of higher education (including through use of subawards and consultant arrangements), the proposer must explain the benefit(s) to the project of performance at the international branch campus, and justify why the project activities cannot be performed at the US campus. Non-profit, non-academic organizations: Independent museums, observatories, research laboratories, professional societies and similar organizations located in the U.S. that are directly associated with educational or research activities.

Who May Serve as PI:

There are no restrictions or limits.

Limit on Number of Proposals per Organization:

Limit on Number of Proposals per PI or co-PI: 2

In each annual competition, an investigator may participate as Principal Investigator (PI), co-Principal Investigator (co-PI), Project Director (PD), Senior/Key Personnel or Consultant in no more than two proposals submitted in response to each annual deadline in this solicitation. These eligibility constraints will be strictly enforced in order to treat everyone fairly and consistently . In the event that an individual exceeds this limit, proposals received within the limit will be accepted based on earliest date and time of proposal submission (i.e., the first two proposals received will be accepted, and the remainder will be returned without review). No exceptions will be made. Proposals submitted in response to this solicitation may not duplicate or be substantially similar to other proposals concurrently under consideration by NSF or NIH programs or study sections. Duplicate or substantially similar proposals will be returned without review. NIH will not accept any application that is essentially the same as one already reviewed within the past 37 months (as described in the NIH Grants Policy Statement ), except for submission: To an NIH Requests for Applications (RFA) of an application that was submitted previously as an investigator-initiated application but not paid; Of an NIH investigator-initiated application that was originally submitted to an RFA but not paid; or Of an NIH application with a changed grant activity code.

Proposal Preparation and Submission Instructions

A. proposal preparation instructions.

• Letters of Intent: Not required
• Preliminary Proposal Submission: Not required

Full Proposals:

• Full Proposals submitted via Research.gov: NSF Proposal and Award Policies and Procedures Guide (PAPPG) guidelines apply. The complete text of the PAPPG is available electronically on the NSF website at: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg .
• Full Proposals submitted via Grants.gov: NSF Grants.gov Application Guide: A Guide for the Preparation and Submission of NSF Applications via Grants.gov guidelines apply (Note: The NSF Grants.gov Application Guide is available on the Grants.gov website and on the NSF website at: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=grantsgovguide ).

B. Budgetary Information

Cost Sharing Requirements:

Inclusion of voluntary committed cost sharing is prohibited.

Indirect Cost (F&A) Limitations:

For NSF, Proposal & Award Policies & Procedures Guide (PAPPG) Guidelines apply.

For NIH, indirect costs on foreign subawards/subcontracts will be limited to eight (8) percent.

Other Budgetary Limitations:

Other budgetary limitations apply. Please see the full text of this solicitation for further information.

C. Due Dates

Proposal review information criteria.

Merit Review Criteria:

National Science Board approved criteria. Additional merit review criteria apply. Please see the full text of this solicitation for further information.

Award Administration Information

Award Conditions:

Additional award conditions apply. Please see the full text of this solicitation for further information.

Reporting Requirements:

Additional reporting requirements apply. Please see the full text of this solicitation for further information.

I. Introduction

The need for a significant transformation in medical, public health and healthcare delivery approaches has been recognized by numerous organizations and captured in a number of reports. For example, the Networking and Information Technology Research and Development (NITRD) program released the Federal Health Information Technology Research and Development Strategic Framework in 2020 that pointed to an overwhelming need for the integration between the computing, informatics, engineering, mathematics and statistics, behavioral and social science disciplines, and the biomedical, and public health research communities to produce the innovation necessary to improve the health of the country. Recent developments and significant advances in machine learning (ML), artificial intelligence (AI), deep learning, high performance and cloud computing, and availability of new datasets make such integration achievable, as documented in the 2023 updated National Artificial Intelligence Research and Development Strategic Plan .

These anticipated transformations hinge on scientific and engineering innovations by interdisciplinary teams that intelligently collect, connect, analyze and interpret data from individuals, devices, and systems to enable discovery and optimize health. Technical challenges include a range of issues, including effective data generation, analysis and automation for a range of biomedical devices (from imaging through medical devices) and systems (e.g., electronic health records) and consumer devices (including the Internet of Things), as well as new technology to generate knowledge. Underlying these challenges are many fundamental scientific and engineering issues that require investment in interdisciplinary research to actualize the transformations, which is the goal of this solicitation.

II. Program Description

This interagency solicitation is a collaboration between NSF and the NIH. The Smart Health program supports innovative, high-risk/high-reward research with the promise of disruptive transformations in biomedical and public health research, which can only be achieved by well-coordinated, convergent, and interdisciplinary approaches that draw from multiple domains of computer and information science, engineering, mathematical sciences and the biomedical, social, behavioral, and economic sciences. Therefore, the work to be funded by this solicitation must make fundamental scientific or engineering contributions to two or more disciplines , such as computer or information sciences, engineering, mathematical sciences, statistics, social, behavioral, or cognitive sciences to improve fundamental understanding of human biological, biomedical, public health and/or health-related processes and address a key health problem. The research teams must include members with appropriate and demonstrable expertise in the major areas involved in the work. Traditional disease-centric medical, clinical, pharmacological, biological or physiological studies and evaluations are outside the scope of this solicitation. In addition, fundamental biological research with humans that also does not advance other fundamental science or engineering areas is out of scope for this program. Finally, proposals addressing health indirectly in the education or work environment are also out of scope.

Generating these transformations will require fundamental research and development of new tools, workflows and methods across many dimensions; some of the themes are highlighted below. These themes should be seen as examples and not exhaustive.

1. Fairness and Trustworthiness: Advancing fairness and trustworthiness in modeling in AI/ML is a highly interdisciplinary endeavor. Real world considerations go beyond the analytics and can inform new directions for computational science to better realize the benefits of algorithmic and data fairness and trustworthiness. The complexity of biomedical and health systems requires deeper understanding of causality in AI/ML models; new ways of integrating social and economic data to address disparities and improve equity, such as disease heterogeneity, disease prevention, resilience, and treatment response, while systematically accounting for a broad range of uncertainties; and new insights into human-AI systems for clinical decision support. In general, this thrust supports the conduct of fundamental computational research into theories, techniques, and methodologies that go well beyond today's capabilities and are motivated by challenges and requirements in biomedical applications.

2. Transformative Analytics in Biomedical and Behavioral Research : As biomedical and behavioral research continues to generate large amount of multi-level and multi-scale data (e.g., clinical, imaging, personal, social, contextual, environmental, and organizational data), challenges remain. New development in areas such as artificial intelligence and machine learning (AI/ML), natural language technologies (NLT), mathematics and statistics and/or quantum information science (QIS) also bring opportunities to address important biomedical and behavioral problems. This theme will support efforts to push forward the current frontline of AI/ML and advanced analytics for biomedical and behavioral research including:

• novel data reduction methods;
• new robust knowledge representations, visualizations, reasoning algorithms, optimization, modeling and inference methods to support development of innovative models for the study of health and disease;
• new computational approaches with provable mathematical guarantees for fusion and analysis of complex behavioral, biomedical and image data to improve inference accuracy, especially in scenarios of noisy and limited data records;
• novel explainable and interpretable AI/ML model development;
• advanced data management systems with the capability to deal with security, privacy and provenance issues;
• novel data systems to build a connected and modernized biomedical data ecosystem;
• development of novel technologies to extract information from unstructured text data such as clinical notes, radiology and pathology reports;
• development of novel simulation and modeling methods to aid in the design and evaluation of new assessments, treatments and medical devices; and
• novel QIS approaches to unique challenges in biomedical and behavioral research.

3. Next Generation Multimodal and Reconfigurable Sensing Systems : This theme addresses the need for new multimodal and reconfigurable sensing systems/platforms and analytics to generate predictive and personalized models of health. The next generation of sensor systems for smart health must have just-in-time monitoring of biomarkers from multiple sensor modalities (e.g., electrochemical, electromagnetic, mechanical, optical, acoustic, etc.) interfaced with different platforms (e.g., mobile, wearable, and implantable). Existing sensor systems generally operate either in discrete formats or with limited inter-connectivity, and are limited in accuracy, selectivity, reliability and data throughput. Those limitations can be overcome by integrating heterogeneous sensing modalities and by having field-adaptive reconfigurable sensor microsystems. This theme encourages the design and fabrication of multimodal and/or reconfigurable sensor systems through innovative research on novel functional materials, devices and circuits for sensing or active interrogation of system states, imaging, communications, and computing. Areas of interest include miniaturized sensor microsystems with integrated signal processing and communication functionalities. Another area of interest is multimodal or reconfigurable sensor systems with dramatically reduced power consumption to extend battery lifetime and enable self-powered operation, making the sensor systems suitable for wearable and implantable applications. Other areas of interest include real-time monitoring of analytes and new biorecognition elements that can be reconfigured to target different analytes on-demand. This thrust also requires researchers to integrate data generated by the multimodal sensor systems, as well as data from other sources, such as laboratory generated data (e.g., genomics, proteomics, etc.), patient-reported outcomes, electronic health records, and existing data sources.

4. Cyber-Physical Systems : Development and adoption of automation has lagged in the biomedical and public health communities. Cyber-physical systems (CPS) are controlled systems built from, and dependent upon, the seamless integration of computation and physical components. These data-rich systems enable new and higher degrees of automation and autonomy. Thus, this theme supports work that enables the creation of closed-loop or human-in- the-loop CPS systems to assess, treat and reduce adverse health events or behaviors, with core research areas including control, data analytics, and machine learning including real-time learning for control, autonomy, design, Internet of Things (IoT), networking, privacy, real-time systems, safety, security, and verification. Finally, development of automated technology that can be utilized across a range of settings (e.g., home, primary care, schools, criminal justice system, child welfare agencies, community-based organizations) to optimize the delivery of effective health interventions is also within scope of the theme.

5. Robotics: This theme addresses the need for novel robotics to provide support and/or automation to enhance health, lengthen lifespan and reduce illness, enhance social connectedness and reduce disabilities. The theme encourages research on robotic systems that exhibit significant levels of both computational capability and physical complexity. Robots are defined as intelligence embodied in an engineered construct, with the ability to process information, sense, plan, and move within or substantially alter its working environment. Here intelligence includes a broad class of methods that enable a robot to solve problems or to make contextually appropriate decisions and act upon them. Currently, robotic devices in health have focused on limited areas (e.g., surgical robotics and exoskeletons). This theme welcomes a wide range of robotic areas, as well as research that considers inextricably interwoven questions of intelligence, computation, and embodiment. The next generation of robotic systems for smart health will need to also have to consider human-robot interaction to enhance usability and effectiveness.

6. Biomedical Image interpretation . This theme's goal is to determine how characteristics of human pattern recognition, visual search, perceptual learning, attentional biases, etc. can inform and improve image interpretation. This theme would include using and developing presentation modalities (e.g., pathologists reading optical slides through a microscope vs. digital whole-slide imagery) and identifying the sources of inter- and intra-observer variability. The theme encourages development of models of how multi-modal contextual information (e.g., integrating patient history, omics, etc. with imaging data) changes the perception of complex images. It also supports new methods to exploit experts' implicit knowledge to improve perceptual decision making (e.g., via rapid gist extraction, context-guided search, etc.). Research on optimal methods for conveying 3D (and 4D) information about anatomy and physiology to human observers is also welcome. This theme also supports advances in image data compression algorithm development to enable more efficient data storage.

7. Unpacking Health Disparities and Health Equity . The National Academies of Sciences, Engineering, and Medicine report, Communities in Action: Pathways to Health Equity (2017), offers a broader context to understand health disparities. In this theme, proposals should seek to develop holistic, data-driven AI/ML or mathematical models to address the structural and/or social determinants of health. Proposers can also develop novel and effective strategies to measure, reduce, and mitigate the effects and impacts of discrimination on health outcomes. The theme also supports new interdisciplinary computational and engineering approaches and models to better understand culture, context and person-centered solutions with diverse communities. To generate technology that is usable and effective will require development of new approaches that support users across socio-economic status, digital and health literacy, technology and broadband access, geography, gender, and ethnicity. Finally, the theme supports development of novel methods of distinguishing the complex pathways between and/or among levels of influence and domains as outlined by the National Institute of Minority Health and Health Disparities Research Framework .

The above listed themes are to provide examples for possible research activities that may be supported by this solicitation, but by no means should the proposed research activities be restricted to these themes. These research themes are clearly not mutually exclusive, and a given project may address multiple themes. This solicitation aims to support research activities that complement rather than duplicate the core programs of the NSF Directorates and the NIH Institutes and Centers and the research efforts supported by other agencies such as the Agency for Healthcare Research and Quality.

NSF supports investigation of fundamental research questions with broadly applicable results. The Smart Health program supports research evaluation with humans. Because advancing fundamental science is early-stage research, randomized control trials are not appropriate for this solicitation and will not be funded. Research that has advanced to a stage that requires randomized control trials should be submitted to an agency whose mission is to improve health.

NIH supports research and discovery that improve human health and save lives. This joint program focuses on fundamental research of generalizable, disease-agnostic approaches with broadly applicable results that align with NIH’s Strategic Plan for Data Science and National Institute of Minority Health and Health Disparities Research Framework.

Integrative Innovation:

Proposals submitted to this solicitation must be integrative and undertake research addressing key application areas by solving problems in multiple scientific domains. The work must make fundamental scientific or engineering contributions to two or more disciplines , such as computer or information sciences, engineering, mathematical sciences, social, behavioral, cognitive and/or economic sciences and address a key health problem. For example, these projects are expected to advance understanding of how computing, engineering and mathematics, combined with advances in behavioral and social science research, would support transformations in health, medicine and/or healthcare and improve the quality of life. Projects are expected to include students and postdocs. Project descriptions must be comprehensive and well-integrated, and should make a convincing case that the collaborative contributions of the project team will be greater than the sum of each of their individual contributions. Collaborations with researchers in the health application domains are required. Such collaborations typically involve multiple institutions, but this is not required. Because the successes of collaborative research efforts are known to depend on thoughtful collaboration mechanisms that regularly bring together the various participants of the project, a Collaboration Plan is required for ALL proposals . Projects will be funded for up to a four-year period and for up to a total of $300,000 per year. The proposed budget should be commensurate with the corresponding scope of work. Rationale must be provided to explain why a budget of the requested size is required to carry out the proposed work.

III. Award Information

Estimated program budget, number of awards and average award size/duration are subject to the availability of funds. An estimated 10 to 16 projects will be funded, subject to availability of funds. Up to $15,000,000-20,000,000 of NSF funds will be invested in proposals submitted to this solicitation. The number of awards and program budgets are subject to the availability of funds.

All awards under this solicitation made by NSF will be as grants or cooperative agreements as determined by the supporting agency. All awards under this solicitation made by NIH will be as grants or cooperative agreements.

Scientists from all disciplines are encouraged to participate. Projects will be awarded depending on the availability of funds and with consideration for creating a balanced overall portfolio.

IV. Eligibility Information

V. proposal preparation and submission instructions.

Full Proposal Preparation Instructions : Proposers may opt to submit proposals in response to this Program Solicitation via Research.gov or Grants.gov.

• Full Proposals submitted via Research.gov: Proposals submitted in response to this program solicitation should be prepared and submitted in accordance with the general guidelines contained in the NSF Proposal and Award Policies and Procedures Guide (PAPPG). The complete text of the PAPPG is available electronically on the NSF website at: https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg . Paper copies of the PAPPG may be obtained from the NSF Publications Clearinghouse, telephone (703) 292-8134 or by e-mail from [email protected] . The Prepare New Proposal setup will prompt you for the program solicitation number.
• Full proposals submitted via Grants.gov: Proposals submitted in response to this program solicitation via Grants.gov should be prepared and submitted in accordance with the NSF Grants.gov Application Guide: A Guide for the Preparation and Submission of NSF Applications via Grants.gov . The complete text of the NSF Grants.gov Application Guide is available on the Grants.gov website and on the NSF website at: ( https://www.nsf.gov/publications/pub_summ.jsp?ods_key=grantsgovguide ). To obtain copies of the Application Guide and Application Forms Package, click on the Apply tab on the Grants.gov site, then click on the Apply Step 1: Download a Grant Application Package and Application Instructions link and enter the funding opportunity number, (the program solicitation number without the NSF prefix) and press the Download Package button. Paper copies of the Grants.gov Application Guide also may be obtained from the NSF Publications Clearinghouse, telephone (703) 292-8134 or by e-mail from [email protected] .

In determining which method to utilize in the electronic preparation and submission of the proposal, please note the following:

Collaborative Proposals. All collaborative proposals submitted as separate submissions from multiple organizations must be submitted via Research.gov. PAPPG Chapter II.E.3 provides additional information on collaborative proposals.

See PAPPG Chapter II.D.2 for guidance on the required sections of a full research proposal submitted to NSF. Please note that the proposal preparation instructions provided in this program solicitation may deviate from the PAPPG instructions.

The following information SUPPLEMENTS (note that it does NOT replace) the guidelines provided in the NSF Proposal & Award Policies & Procedures Guide (PAPPG).

Proposal Titles: Proposal titles must begin with SCH , followed by a colon and the title of the project (i.e., SCH: Title ). If you submit a proposal as part of a set of collaborative proposals, the title of the proposal should begin with Collaborative Research followed by a colon, then SCH followed by a colon, and the title. For example, if you are submitting a collaborative set of proposals, then the title of each would be Collaborative Research: SCH: Title.

Proposals from PIs in institutions that have Research in Undergraduate Institutions (RUI) eligibility should have a proposal title that begins with Collaborative Research (if applicable), followed by a colon, then SCH followed by a colon, then RUI followed by a colon, and then the title, for example, Collaborative Research: SCH: RUI: Title.

Project Summary (1 page limit): At the beginning of the Overview section of the Project Summary enter the title of the Smart Health project, the name of the PI and the lead institution. The Project Summary must include three labeled sections: Overview, Intellectual Merit and Broader Impacts. The overview includes a description of the project. Intellectual Merit should describe the transformative research and the potential of the proposed activity to advance knowledge. Broader Impacts should describe the potential of the proposed activity to benefit society and contribute to the achievement of specific, desired societal outcomes. The Broader Impacts can include education goals, and the community (communities) that will be impacted by its results.

Project Description: There is a 15-page limit for all proposals. Within the project description, include a section labeled 'Evaluation Plan' that includes a description of how the team will evaluate the proposed science/engineering . This plan could include results from applications of the research to specific outcomes in health domain, efficacy studies, assessments of learning and engagement, and other such evaluation. The proposed Evaluation Plan should be appropriate for the size and scope of the project.

Please note that the Collaboration Plan must be submitted as a Supplementary Document for this solicitation; see guidance below.

Proposal Budget: It is expected that the PIs, co-PIs, and other team members funded by the project will attend an SCH PI meeting annually to present project research findings and capacity-building or community outreach activities. Requested budgets should include funds for travel to this annual event for at least one project PI.

Supplementary Documents: In the Supplementary Documents Section, upload the following:

• Collaboration Plan. Proposals must include a Collaboration Plan. The Collaboration Plan must be submitted as a supplementary document and cannot exceed two pages. Proposals that do not include a Collaboration Plan will be returned without review . The Collaboration Plan must be labeled "Collaboration Plan" and must include: 1) the specific roles of the collaborating PIs, co-PIs, other Senior/Key Personnel and paid consultants at all organizations involved; 2) how the project will be managed across institutions and disciplines; 3) identification of the specific collaboration mechanisms that will enable cross-institution and/or cross-discipline scientific integration (e.g., workshops, graduate student exchange, project meetings at conferences, use of videoconferencing and other communication tools, software repositories, etc.); and 4) specific references to the budget line items that support these collaboration mechanisms.
• Human Subjects Protection. Proposals involving human subjects should include a supplementary document of no more than two pages in length summarizing potential risks to human subjects; plans for recruitment and informed consent; inclusion of women, minorities, and children; and planned procedures to protect against or minimize potential risks. Human subjects plans must include the NIH enrollment table ( https://era.nih.gov/erahelp/assist/Content/ASSIST_Help_Topics/3_Form_Screens/PHS_HS_CT/Incl_Enroll_Rprt.htm , please see the Planned enrollment table for the expected format).
• Detailed description and justification of the proposed use of the animals, including species, strains, ages, sex, and number to be used;
• Information on the veterinary care of the animals;
• Description of procedures for minimizing discomfort, distress, pain, and injury; and
• Method of euthanasia and the reasons for its selection.
• Data Management and Sharing Plan (required). This supplementary document should describe how the proposal will conform to NSF policy on the dissemination and sharing of research results. See Chapter II.D.2 of the PAPPG for full policy implementation and the section on Data Management and Sharing Plans. For additional information on the Dissemination and Sharing of Research Results, see: https://www.nsf.gov/bfa/dias/policy/dmp.jsp . For specific guidance for Data Management and Sharing Plans submitted to the Directorate for Computer and Information Science and Engineering (CISE) see: https://www.nsf.gov/cise/cise_dmp.jsp .
• Documentation of Collaborative Arrangements of Significance to the Proposal through Letters of Collaboration. There are two types of collaboration, one involving individuals/organizations that are included in the budget, and the other involving individuals/organizations that are not included in the budget. Collaborations that are included in the budget should be described in the Project Description and the Collaboration Plan. Any substantial collaboration with individuals/organizations not included in the budget should be described in the Facilities, Equipment and Other Resources section of the proposal (see PAPPG Chapter II.D.2.g). In either case, whether or not the collaborator is included in the budget, a letter of collaboration from each named participating organization other than the submitting lead, non-lead, and/or subawardee institutions must be provided at the time of submission of the proposal. Such letters describe the nature of the collaboration and what the collaborator(s) brings to the project. They must explicitly state the nature of the collaboration, appear on the organization's letterhead, and be signed by the appropriate organizational representative. These letters must not otherwise deviate from the restrictions and requirements set forth in the PAPPG, Chapter II.D.2.i. Please note that letters of support, that do not document collaborative arrangements of significance to the project, but primarily convey a sense of enthusiasm for the project and/or highlight the qualifications of the PI or co-PI may not be submitted, and reviewers will be instructed not to consider these letters in reviewing the merits of the proposal.
• List of Project Personnel and Partner Institutions (Note - In collaborative proposals, only the lead institution should provide this information). Provide current, accurate information for all personnel and institutions involved in the project. NSF staff will use this information in the merit review process to manage reviewer selection. The list should include all PIs, co-PIs, Senior/Key Personnel, paid/unpaid Consultants or Collaborators, Subawardees, Postdocs, and project-level advisory committee members. This list should be numbered and include (in this order) Full name, Organization(s), and Role in the project, with each item separated by a semi-colon. Each person listed should start a new numbered line. For example: 1. Mei Lin; XYZ University; PI 2. Jak Jabes; University of PQR; Senior/Key Personnel 3. Jane Brown; XYZ University; Postdoctoral Researcher 4. Rakel Ademas; ABC Inc.; Paid Consultant 5. Maria Wan; Welldone Institution; Unpaid Collaborator 6. Rimon Greene; ZZZ University; Subawardee
• Mentoring Plan (if applicable). Each proposal that requests funding to support postdoctoral scholars or graduate students must include, as a supplementary document, a description of the mentoring activities that will be provided for such individuals. Please be advised that if required, NSF systems will not permit submission of a proposal that is missing a Mentoring Plan. See Chapter II.D.2.i of the PAPPG for further information about the implementation of this requirement.
• Other Specialized Information. RUI Proposals: PIs from predominantly undergraduate institutions should follow the instructions https://new.nsf.gov/funding/opportunities/facilitating-research-primarily-undergraduate .

Single Copy Documents:

(1) Collaborators and Other Affiliations Information.

Proposers should follow the guidance specified in Chapter II.D.2.h of the NSF PAPPG. Grants.gov Users: The COA information must be provided through use of the COA template and uploaded as a PDF attachment.

Note the distinction to the list of Project Personnel and Partner Institutions specified above under Supplementary Documents: the listing of all project participants is collected by the project lead and entered as a Supplementary Document, which is then automatically included with all proposals in a project. The Collaborators and Other Affiliations are entered for each participant within each proposal and, as Single Copy Documents, are available only to NSF staff.

SCH Proposal Preparation Checklist:

The following checklist is provided as a reminder of the items that should be verified before submitting a proposal to this solicitation. This checklist is a summary of the requirements described above and in the PAPPG and does not replace the complete set of requirements in the PAPPG. For the items marked with (RWR), the proposal will be returned without review if the required item is noncompliant at the submission deadline.

• (RWR) A two-page Collaboration Plan must be included as a Supplementary Document.
• Letters of Collaboration are permitted as Supplementary Documents.
• (RWR) Project Summary not to exceed one page that includes three labeled sections: Overview, Intellectual Merit and Broader Impacts.
• (RWR) Within the Project Description, a section labeled “Broader Impacts” that describes the potential to benefit society and contribute to the achievement of specific, desired societal outcomes.
• Within the Project Description, a section labeled Evaluation Plan that details how the project will be evaluated.
• (RWR) Within the Project Description, a description of "Results from Prior NSF Support”.
• (RWR) If the budget includes postdoctoral scholars or graduate students, a one-page Mentoring Plan must be included as a Supplementary Document.
• A list of Project Personnel and Partner Institutions is required as a Supplementary Document.
• (RWR) A Data Management and Sharing Plan, not to exceed two pages, must be included as a Supplementary Document.
• Proposals involving human subjects or animals, should include a Human Subjects Plan with an NIH Enrollment Table or Vertebrate Animals Plan as a Supplementary Document.
• Collaborators & Other Affiliations (COA) for each PI, co-PI, and Senior/Key Personnel should be submitted using the spreadsheet template uploaded as Single Copy Documents.

Cost Sharing:

Indirect Cost (F&A) Limitations:

Budgets should include travel funds to attend one SCH PI meeting annually for the project PIs, co-PIs and other team members as appropriate from all collaborating institutions.

D. Research.gov/Grants.gov Requirements

For Proposals Submitted Via Research.gov:

To prepare and submit a proposal via Research.gov, see detailed technical instructions available at: https://www.research.gov/research-portal/appmanager/base/desktop?_nfpb=true&_pageLabel=research_node_display&_nodePath=/researchGov/Service/Desktop/ProposalPreparationandSubmission.html . For Research.gov user support, call the Research.gov Help Desk at 1-800-381-1532 or e-mail [email protected] . The Research.gov Help Desk answers general technical questions related to the use of the Research.gov system. Specific questions related to this program solicitation should be referred to the NSF program staff contact(s) listed in Section VIII of this funding opportunity.

For Proposals Submitted Via Grants.gov:

Before using Grants.gov for the first time, each organization must register to create an institutional profile. Once registered, the applicant's organization can then apply for any federal grant on the Grants.gov website. Comprehensive information about using Grants.gov is available on the Grants.gov Applicant Resources webpage: https://www.grants.gov/web/grants/applicants.html . In addition, the NSF Grants.gov Application Guide (see link in Section V.A) provides instructions regarding the technical preparation of proposals via Grants.gov. For Grants.gov user support, contact the Grants.gov Contact Center at 1-800-518-4726 or by email: [email protected] . The Grants.gov Contact Center answers general technical questions related to the use of Grants.gov. Specific questions related to this program solicitation should be referred to the NSF program staff contact(s) listed in Section VIII of this solicitation.

Submitting the Proposal: Once all documents have been completed, the Authorized Organizational Representative (AOR) must submit the application to Grants.gov and verify the desired funding opportunity and agency to which the application is submitted. The AOR must then sign and submit the application to Grants.gov. The completed application will be transferred to Research.gov for further processing.

The NSF Grants.gov Proposal Processing in Research.gov informational page provides submission guidance to applicants and links to helpful resources including the NSF Grants.gov Application Guide , Grants.gov Proposal Processing in Research.gov how-to guide , and Grants.gov Submitted Proposals Frequently Asked Questions . Grants.gov proposals must pass all NSF pre-check and post-check validations in order to be accepted by Research.gov at NSF.

When submitting via Grants.gov, NSF strongly recommends applicants initiate proposal submission at least five business days in advance of a deadline to allow adequate time to address NSF compliance errors and resubmissions by 5:00 p.m. submitting organization's local time on the deadline. Please note that some errors cannot be corrected in Grants.gov. Once a proposal passes pre-checks but fails any post-check, an applicant can only correct and submit the in-progress proposal in Research.gov.

Proposers that submitted via Research.gov may use Research.gov to verify the status of their submission to NSF. For proposers that submitted via Grants.gov, until an application has been received and validated by NSF, the Authorized Organizational Representative may check the status of an application on Grants.gov. After proposers have received an e-mail notification from NSF, Research.gov should be used to check the status of an application.

VI. NSF Proposal Processing And Review Procedures

Proposals received by NSF are assigned to the appropriate NSF program for acknowledgement and, if they meet NSF requirements, for review. All proposals are carefully reviewed by a scientist, engineer, or educator serving as an NSF Program Officer, and usually by three to ten other persons outside NSF either as ad hoc reviewers, panelists, or both, who are experts in the particular fields represented by the proposal. These reviewers are selected by Program Officers charged with oversight of the review process. Proposers are invited to suggest names of persons they believe are especially well qualified to review the proposal and/or persons they would prefer not review the proposal. These suggestions may serve as one source in the reviewer selection process at the Program Officer's discretion. Submission of such names, however, is optional. Care is taken to ensure that reviewers have no conflicts of interest with the proposal. In addition, Program Officers may obtain comments from site visits before recommending final action on proposals. Senior NSF staff further review recommendations for awards. A flowchart that depicts the entire NSF proposal and award process (and associated timeline) is included in PAPPG Exhibit III-1.

A comprehensive description of the Foundation's merit review process is available on the NSF website at: https://www.nsf.gov/bfa/dias/policy/merit_review/ .

Proposers should also be aware of core strategies that are essential to the fulfillment of NSF's mission, as articulated in Leading the World in Discovery and Innovation, STEM Talent Development and the Delivery of Benefits from Research - NSF Strategic Plan for Fiscal Years (FY) 2022 - 2026 . These strategies are integrated in the program planning and implementation process, of which proposal review is one part. NSF's mission is particularly well-implemented through the integration of research and education and broadening participation in NSF programs, projects, and activities.

One of the strategic objectives in support of NSF's mission is to foster integration of research and education through the programs, projects, and activities it supports at academic and research institutions. These institutions must recruit, train, and prepare a diverse STEM workforce to advance the frontiers of science and participate in the U.S. technology-based economy. NSF's contribution to the national innovation ecosystem is to provide cutting-edge research under the guidance of the Nation's most creative scientists and engineers. NSF also supports development of a strong science, technology, engineering, and mathematics (STEM) workforce by investing in building the knowledge that informs improvements in STEM teaching and learning.

NSF's mission calls for the broadening of opportunities and expanding participation of groups, institutions, and geographic regions that are underrepresented in STEM disciplines, which is essential to the health and vitality of science and engineering. NSF is committed to this principle of diversity and deems it central to the programs, projects, and activities it considers and supports.

A. Merit Review Principles and Criteria

The National Science Foundation strives to invest in a robust and diverse portfolio of projects that creates new knowledge and enables breakthroughs in understanding across all areas of science and engineering research and education. To identify which projects to support, NSF relies on a merit review process that incorporates consideration of both the technical aspects of a proposed project and its potential to contribute more broadly to advancing NSF's mission "to promote the progress of science; to advance the national health, prosperity, and welfare; to secure the national defense; and for other purposes." NSF makes every effort to conduct a fair, competitive, transparent merit review process for the selection of projects.

1. Merit Review Principles

These principles are to be given due diligence by PIs and organizations when preparing proposals and managing projects, by reviewers when reading and evaluating proposals, and by NSF program staff when determining whether or not to recommend proposals for funding and while overseeing awards. Given that NSF is the primary federal agency charged with nurturing and supporting excellence in basic research and education, the following three principles apply:

• All NSF projects should be of the highest quality and have the potential to advance, if not transform, the frontiers of knowledge.
• NSF projects, in the aggregate, should contribute more broadly to achieving societal goals. These "Broader Impacts" may be accomplished through the research itself, through activities that are directly related to specific research projects, or through activities that are supported by, but are complementary to, the project. The project activities may be based on previously established and/or innovative methods and approaches, but in either case must be well justified.
• Meaningful assessment and evaluation of NSF funded projects should be based on appropriate metrics, keeping in mind the likely correlation between the effect of broader impacts and the resources provided to implement projects. If the size of the activity is limited, evaluation of that activity in isolation is not likely to be meaningful. Thus, assessing the effectiveness of these activities may best be done at a higher, more aggregated, level than the individual project.

With respect to the third principle, even if assessment of Broader Impacts outcomes for particular projects is done at an aggregated level, PIs are expected to be accountable for carrying out the activities described in the funded project. Thus, individual projects should include clearly stated goals, specific descriptions of the activities that the PI intends to do, and a plan in place to document the outputs of those activities.

These three merit review principles provide the basis for the merit review criteria, as well as a context within which the users of the criteria can better understand their intent.

2. Merit Review Criteria

All NSF proposals are evaluated through use of the two National Science Board approved merit review criteria. In some instances, however, NSF will employ additional criteria as required to highlight the specific objectives of certain programs and activities.

The two merit review criteria are listed below. Both criteria are to be given full consideration during the review and decision-making processes; each criterion is necessary but neither, by itself, is sufficient. Therefore, proposers must fully address both criteria. (PAPPG Chapter II.D.2.d(i). contains additional information for use by proposers in development of the Project Description section of the proposal). Reviewers are strongly encouraged to review the criteria, including PAPPG Chapter II.D.2.d(i), prior to the review of a proposal.

When evaluating NSF proposals, reviewers will be asked to consider what the proposers want to do, why they want to do it, how they plan to do it, how they will know if they succeed, and what benefits could accrue if the project is successful. These issues apply both to the technical aspects of the proposal and the way in which the project may make broader contributions. To that end, reviewers will be asked to evaluate all proposals against two criteria:

• Intellectual Merit: The Intellectual Merit criterion encompasses the potential to advance knowledge; and
• Broader Impacts: The Broader Impacts criterion encompasses the potential to benefit society and contribute to the achievement of specific, desired societal outcomes.

The following elements should be considered in the review for both criteria:

• Advance knowledge and understanding within its own field or across different fields (Intellectual Merit); and
• Benefit society or advance desired societal outcomes (Broader Impacts)?
• To what extent do the proposed activities suggest and explore creative, original, or potentially transformative concepts?
• Is the plan for carrying out the proposed activities well-reasoned, well-organized, and based on a sound rationale? Does the plan incorporate a mechanism to assess success?
• How well qualified is the individual, team, or organization to conduct the proposed activities?
• Are there adequate resources available to the PI (either at the home organization or through collaborations) to carry out the proposed activities?

Broader impacts may be accomplished through the research itself, through the activities that are directly related to specific research projects, or through activities that are supported by, but are complementary to, the project. NSF values the advancement of scientific knowledge and activities that contribute to achievement of societally relevant outcomes. Such outcomes include, but are not limited to: full participation of women, persons with disabilities, and other underrepresented groups in science, technology, engineering, and mathematics (STEM); improved STEM education and educator development at any level; increased public scientific literacy and public engagement with science and technology; improved well-being of individuals in society; development of a diverse, globally competitive STEM workforce; increased partnerships between academia, industry, and others; improved national security; increased economic competitiveness of the United States; and enhanced infrastructure for research and education.

Proposers are reminded that reviewers will also be asked to review the Data Management and Sharing Plan and the Mentoring Plan, as appropriate.

Additional Solicitation Specific Review Criteria

The proposals will also be evaluated based on:

Collaboration and Management: The work to be funded by this solicitation must make fundamental contributions to two or more disciplines and address a key health problem. The collaboration plan should demonstrate active participation of this multidisciplinary group, which includes, but is not limited to: fundamental science and engineering researchers; biomedical, health and/or clinical researchers; other necessary research expertise; client groups; and, technology vendors/commercial enterprises. The collaboration plan should include the roles and demonstrate the extent to which the group is integrated, has a common focus and the quality of the collaboration plan.

Additional NIH Review Criteria:

The mission of the NIH is to support science in pursuit of knowledge about the biology and behavior of living systems and to apply that knowledge to extend healthy life and reduce the burdens of illness and disability. In their evaluations of scientific merit, reviewers will be asked to consider the following criteria that are used by NIH:

Overall Impact. Reviewers will provide an overall impact/priority score and criterion scores to reflect their assessment of the likelihood for the project to exert a sustained, powerful influence on the research field(s) involved, in consideration of the following five core review criteria, and additional review criteria (as applicable for the project proposed).

Significance. Does the project address an important problem or a critical barrier to progress in the field? If the aims of the project are achieved, how will scientific knowledge, technical capability, and/or clinical practice be improved? How will successful completion of the aims change the concepts, methods, technologies, treatments, services, or preventative interventions that drive this field?

Investigator(s). Are the PD/PIs, collaborators, and other researchers well suited to the project? If Early Stage Investigators or New Investigators, do they have appropriate experience and training? If established, have they demonstrated an ongoing record of accomplishments that have advanced their field(s)? If the project is collaborative or multi-PD/PI, do the investigators have complementary and integrated expertise; are their leadership approach, governance and organizational structure appropriate for the project?

Innovation. Does the application challenge and seek to shift current research or clinical practice paradigms by utilizing novel theoretical concepts, approaches or methodologies, instrumentation, or interventions? Are the concepts, approaches or methodologies, instrumentation, or interventions novel to one field of research or novel in a broad sense? Is a refinement, improvement, or new application of theoretical concepts, approaches or methodologies, instrumentation, or interventions proposed?

Approach. Are the overall strategy, methodology, and analyses well-reasoned and appropriate to accomplish the specific aims of the project? Are potential problems, alternative strategies, and benchmarks for success presented? If the project is in the early stages of development, will the strategy establish feasibility and will particularly risky aspects be managed? If the project involves clinical research, are the plans for 1) protection of human subjects from research risks, and 2) inclusion of women, minorities, and individuals across the lifespan justified in terms of the scientific goals and research strategy proposed?

Environment. Will the scientific environment in which the work will be done contribute to the probability of success? Are the institutional support, equipment and other physical resources available to the investigators adequate for the project proposed? Will the project benefit from unique features of the scientific environment, subject populations, or collaborative arrangements? Where applicable, the following items will also be considered:

Protections for Human Subjects. For research that involves human subjects but does not involve one of the six categories of research that are exempt under 45 CFR Part 46, the committee will evaluate the justification for involvement of human subjects and the proposed protections from research risk relating to their participation according to the following five review criteria: 1) risk to subjects, 2) adequacy of protection against risks, 3) potential benefits to the subjects and others, 4) importance of the knowledge to be gained, and 5) data and safety monitoring. For research that involves human subjects and meets the criteria for one or more of the six categories of research that are exempt under 45 CFR Part 46, the committee will evaluate: 1) the justification for the exemption, 2) human subjects involvement and characteristics, and 3) sources of materials. For additional information on review of the Human Subjects section, please refer to the Human Subjects Protection and Inclusion Guidelines.

Inclusion of Women, Minorities, and Individuals Across the Lifespan . When the proposed project involves human subjects and/or NIH-defined clinical research, the committee will evaluate the proposed plans for the inclusion (or exclusion) of individuals on the basis of sex/gender, race, and ethnicity, as well as the inclusion (or exclusion) of individuals of all ages (including children and older adults) to determine if it is justified in terms of the scientific goals and research strategy proposed. For additional information on review of the Inclusion section, please refer to the Guidelines for the Review of Inclusion in Clinical Research .

Vertebrate Animals. The committee will evaluate the involvement of live vertebrate animals as part of the scientific assessment according to the following four points: 1) description and justification of the proposed use of the animals, and species, strains, ages, sex, and numbers to be used; 2) adequacy of veterinary care; 3) procedures for minimizing discomfort, distress, pain and injury; and 4) methods of euthanasia and reason for selection if not consistent with the AVMA Guidelines on Euthanasia. For additional information, see http://grants.nih.gov/grants/olaw/VASchecklist.pdf .

Biohazards. Reviewers will assess whether materials or procedures proposed are potentially hazardous to research personnel and/or the environment, and if needed, determine whether adequate protection is proposed.

Budget and Period of Support (non-score-driving). Reviewers will consider whether the budget and the requested period of support are fully justified and reasonable in relation to the proposed research.

B. Review and Selection Process

Proposals submitted in response to this program solicitation will be reviewed by Ad hoc Review and/or Panel Review, or Joint agency review.

Proposals submitted in response to this program solicitation will be reviewed by Ad hoc Review and/or Panel Review, or joint agency review.

Reviewers will be asked to evaluate proposals using two National Science Board approved merit review criteria and, if applicable, additional program specific criteria. A summary rating and accompanying narrative will generally be completed and submitted by each reviewer and/or panel. The Program Officer assigned to manage the proposal's review will consider the advice of reviewers and will formulate a recommendation.

NSF will manage and conduct the peer review process for this solicitation; the National Institutes of Health will only observe the review process and have access to proposal and review information, such as unattributed reviews and panel summaries. NIH and NSF will make independent award decisions.

After scientific, technical and programmatic review and consideration of appropriate factors, the NSF Program Officer recommends to the cognizant Division Director whether the proposal should be declined or recommended for award. NSF strives to be able to tell applicants whether their proposals have been declined or recommended for funding within six months. Large or particularly complex proposals or proposals from new awardees may require additional review and processing time. The time interval begins on the deadline or target date, or receipt date, whichever is later. The interval ends when the Division Director acts upon the Program Officer's recommendation.

After programmatic approval has been obtained, the proposals recommended for funding will be forwarded to the Division of Grants and Agreements or the Division of Acquisition and Cooperative Support for review of business, financial, and policy implications. After an administrative review has occurred, Grants and Agreements Officers perform the processing and issuance of a grant or other agreement. Proposers are cautioned that only a Grants and Agreements Officer may make commitments, obligations or awards on behalf of NSF or authorize the expenditure of funds. No commitment on the part of NSF should be inferred from technical or budgetary discussions with a NSF Program Officer. A Principal Investigator or organization that makes financial or personnel commitments in the absence of a grant or cooperative agreement signed by the NSF Grants and Agreements Officer does so at their own risk.

Once an award or declination decision has been made, Principal Investigators are provided feedback about their proposals. In all cases, reviews are treated as confidential documents. Verbatim copies of reviews, excluding the names of the reviewers or any reviewer-identifying information, are sent to the Principal Investigator/Project Director by the Program Officer. In addition, the proposer will receive an explanation of the decision to award or decline funding.

Review Process and Deviations from the NSF PAPPG

This section provides agency-specific guidance for the SCH program.

NSF will take the lead in organizing and conducting the review process in compliance with the Federal Advisory Committee Act.

In addition to any conflict forms required by NSF, an NIH Post-Review Certification Form will be circulated at or near the end of the second day of the review meeting and collected by the NIH Scientific Review Officer (SRO). By signing the Post-Review Certification Form, panelists will certify for NIH that confidentiality and conflict-of-interest procedures have been followed. Conflicts of interest are handled in a manner similar to NSF procedures: those in conflict will be asked to step out of the room, or as appropriate, NSF's Designated Ethics Official or designee may recommend remedies to resolve specific conflicts on a case by case basis. Co-investigators and investigators that would directly benefit should the grant be awarded are ineligible to serve as reviewers.

Approximately seven to 10 review panels, equivalent to NIH study sections, will be organized each year, with the exact number and topical clustering of panels determined according to the number and topical areas of the proposals received. Panel management will be conducted by the four NSF directorates, with the majority conducted by CISE. Co-review across clusters, divisions and directorates will be performed where appropriate. SROs from the CSR at the NIH will be assigned to work cooperatively with NSF staff on each proposal panel. Together, they will have the responsibility to work out the details of the review process such that all agencies' needs are met. Before the review panel meetings, the representatives from the NIH SROs will work together with the NSF staff to prepare written instructions for the reviewers and to develop and implement an NIH-like scoring system (1-9) for NIH use on proposal panels. The representatives of all participating NIH Institutes and Centers will also be invited to attend the review meetings to ensure that this review is conducted in a manner that is consistent with the agreements between NSF and the NIH.

After scientific, technical and programmatic review and consideration of appropriate factors, the NSF Program Officer recommends to the cognizant Division Director whether the proposal should be declined or recommended for award. NSF is striving to be able to tell applicants whether their proposals have been declined or recommended for funding within six months. The time interval begins on the deadline or target date, or receipt date, whichever is later. The interval ends when the Division Director accepts the Program Officer's recommendation.

A summary rating and accompanying narrative will be completed and submitted by each reviewer. In all cases, reviews are treated as confidential documents. Verbatim copies of reviews, excluding the names of the reviewers, are sent to the Principal Investigator/Project Director by the Program Officer. In addition, the proposer will receive an explanation of the decision to award or decline funding.

In all cases, after programmatic approval has been obtained, the proposals recommended for funding will be forwarded to the Division of Grants and Agreements for review of business, financial, and policy implications and the processing and issuance of a grant or other agreement. Proposers are cautioned that only a Grants and Agreements Officer may make commitments, obligations or awards on behalf of NSF or authorize the expenditure of funds. No commitment on the part of NSF should be inferred from technical or budgetary discussions with a NSF Program Officer. A Principal Investigator or organization that makes financial or personnel commitments in the absence of a grant or cooperative agreement signed by the NSF Grants and Agreements Officer does so at their own risk.

For those proposals that are selected for potential funding by participating NIH Institutes, the PI will be required to resubmit the proposal in an NIH-approved format. Applicants must then complete the submission process and track the status of the application in the eRA Commons, NIH’s electronic system for grants administration. PIs invited to resubmit to NIH will receive further information on this process from the NIH.

Consistent with the NIH Policy for Data Management and Sharing, when data management and sharing is applicable to the award, recipients will be required to adhere to the Data Management and Sharing requirements as outlined in the NIH Grants Policy Statement . Upon the approval of a Data Management and Sharing Plan, it is required for recipients to implement the plan as described. All applicants planning research (funded by NIH) that results in the generation of scientific data are required to comply with the instructions for the NIH Data Management and Sharing Plan. All applications, regardless of the amount of direct costs requested for any one year, must address a Data Management and Sharing Plan.

An applicant will not be allowed to increase the proposed budget or change the scientific content of the application in the reformatted submission to the NIH. Indirect costs on any foreign subawards/subcontracts will be limited to eight (8) percent. Applicants will be expected to utilize the Multiple Principal Investigator option at the NIH ( https://grants.nih.gov/grants/multi_PI/ ) as appropriate.

To fulfill NIH's need for a list of participating reviewers for Summary Statements without disclosing the specific reviewers of each proposal, NSF will provide an aggregated list of the full set of reviewers for the SCH program to CSR.

Following the NSF peer review, recommended applications that have been resubmitted to the NIH are required to go to second level review by the Advisory Council or Advisory Board of the awarding Institute or Center. The following will be considered in making funding decisions:

• Scientific and technical merit of the proposed project as determined by scientific peer review.
• Availability of funds.
• Relevance of the proposed project to program priorities.
• Adequacy of data management and sharing plans.

Subsequent grant administration procedures for NIH awardees, including those related to New and Early Stage Investigators ( https://grants.nih.gov/policy/early-investigators/index.htm ), will be in accordance with the policies of NIH. Applications selected for NIH funding will use the NIH R or U funding mechanisms. At the end of the project period, renewal applications for projects funded by the NIH are expected to be submitted directly to the NIH as Renewal Applications.

VII. Award Administration Information

A. notification of the award.

Notification of the award is made to the submitting organization by an NSF Grants and Agreements Officer. Organizations whose proposals are declined will be advised as promptly as possible by the cognizant NSF Program administering the program. Verbatim copies of reviews, not including the identity of the reviewer, will be provided automatically to the Principal Investigator. (See Section VI.B. for additional information on the review process.)

B. Award Conditions

An NSF award consists of: (1) the award notice, which includes any special provisions applicable to the award and any numbered amendments thereto; (2) the budget, which indicates the amounts, by categories of expense, on which NSF has based its support (or otherwise communicates any specific approvals or disapprovals of proposed expenditures); (3) the proposal referenced in the award notice; (4) the applicable award conditions, such as Grant General Conditions (GC-1)*; or Research Terms and Conditions* and (5) any announcement or other NSF issuance that may be incorporated by reference in the award notice. Cooperative agreements also are administered in accordance with NSF Cooperative Agreement Financial and Administrative Terms and Conditions (CA-FATC) and the applicable Programmatic Terms and Conditions. NSF awards are electronically signed by an NSF Grants and Agreements Officer and transmitted electronically to the organization via e-mail.

*These documents may be accessed electronically on NSF's Website at https://www.nsf.gov/awards/managing/award_conditions.jsp?org=NSF . Paper copies may be obtained from the NSF Publications Clearinghouse, telephone (703) 292-8134 or by e-mail from [email protected] .

More comprehensive information on NSF Award Conditions and other important information on the administration of NSF awards is contained in the NSF Proposal & Award Policies & Procedures Guide (PAPPG) Chapter VII, available electronically on the NSF Website at https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg .

Administrative and National Policy Requirements

Build America, Buy America

As expressed in Executive Order 14005, Ensuring the Future is Made in All of America by All of America’s Workers (86 FR 7475), it is the policy of the executive branch to use terms and conditions of Federal financial assistance awards to maximize, consistent with law, the use of goods, products, and materials produced in, and services offered in, the United States.

Consistent with the requirements of the Build America, Buy America Act (Pub. L. 117-58, Division G, Title IX, Subtitle A, November 15, 2021), no funding made available through this funding opportunity may be obligated for an award unless all iron, steel, manufactured products, and construction materials used in the project are produced in the United States. For additional information, visit NSF’s Build America, Buy America webpage.

Special Award Conditions:

Attribution of support in publications must acknowledge the joint program, as well as the funding organization and award number, by including the phrase, "as part of the NSF/NIH Smart Health and Biomedical Research in the Era of Artificial Intelligence and Advanced Data Science Program."

NIH-Specific Award Conditions: Grants made by NSF will be subject to NSF's award conditions. Grants made by NIH will be subject to NIH's award conditions (see http://grants.nih.gov/grants/policy/awardconditions.htm ).

C. Reporting Requirements

For all multi-year grants (including both standard and continuing grants), the Principal Investigator must submit an annual project report to the cognizant Program Officer no later than 90 days prior to the end of the current budget period. (Some programs or awards require submission of more frequent project reports). No later than 120 days following expiration of a grant, the PI also is required to submit a final annual project report, and a project outcomes report for the general public.

Failure to provide the required annual or final annual project reports, or the project outcomes report, will delay NSF review and processing of any future funding increments as well as any pending proposals for all identified PIs and co-PIs on a given award. PIs should examine the formats of the required reports in advance to assure availability of required data.

PIs are required to use NSF's electronic project-reporting system, available through Research.gov, for preparation and submission of annual and final annual project reports. Such reports provide information on accomplishments, project participants (individual and organizational), publications, and other specific products and impacts of the project. Submission of the report via Research.gov constitutes certification by the PI that the contents of the report are accurate and complete. The project outcomes report also must be prepared and submitted using Research.gov. This report serves as a brief summary, prepared specifically for the public, of the nature and outcomes of the project. This report will be posted on the NSF website exactly as it is submitted by the PI.

More comprehensive information on NSF Reporting Requirements and other important information on the administration of NSF awards is contained in the NSF Proposal & Award Policies & Procedures Guide (PAPPG) Chapter VII, available electronically on the NSF Website at https://www.nsf.gov/publications/pub_summ.jsp?ods_key=pappg .

Additional data may be required for NSF sponsored Cooperative Agreements.

Proposals which are initially funded by NSF at a level of $300,000 of total costs per year up to four years will be evaluated based on the proposed work plan by teams of experts periodically through the term of the project to determine performance levels. All publications, reports, data and other output from all awards must be prepared in digital format and meet general requirements for storage, indexing, searching and retrieval.

Contact the cognizant organization program officer for additional information.

VIII. Agency Contacts

Please note that the program contact information is current at the time of publishing. See program website for any updates to the points of contact.

General inquiries regarding this program should be made to:

For questions related to the use of NSF systems contact:

• NSF Help Desk: 1-800-381-1532
• Research.gov Help Desk e-mail: [email protected]

For questions relating to Grants.gov contact:

• Grants.gov Contact Center: If the Authorized Organizational Representatives (AOR) has not received a confirmation message from Grants.gov within 48 hours of submission of application, please contact via telephone: 1-800-518-4726; e-mail: [email protected] .

IX. Other Information

The NSF website provides the most comprehensive source of information on NSF Directorates (including contact information), programs and funding opportunities. Use of this website by potential proposers is strongly encouraged. In addition, "NSF Update" is an information-delivery system designed to keep potential proposers and other interested parties apprised of new NSF funding opportunities and publications, important changes in proposal and award policies and procedures, and upcoming NSF Grants Conferences . Subscribers are informed through e-mail or the user's Web browser each time new publications are issued that match their identified interests. "NSF Update" also is available on NSF's website .

Grants.gov provides an additional electronic capability to search for Federal government-wide grant opportunities. NSF funding opportunities may be accessed via this mechanism. Further information on Grants.gov may be obtained at https://www.grants.gov .

Main Websites for the Participating Agencies: NATIONAL SCIENCE FOUNDATION https://www.nsf.gov NATIONAL INSTITUTES OF HEALTH http://nih.gov/ PUBLIC BRIEFINGS One or more collaborative webinar briefings with question and answer functionality will be held prior to the first submission deadline date. Schedules will be posted on the sponsor solicitation web sites.

About The National Science Foundation

The National Science Foundation (NSF) is an independent Federal agency created by the National Science Foundation Act of 1950, as amended (42 USC 1861-75). The Act states the purpose of the NSF is "to promote the progress of science; [and] to advance the national health, prosperity, and welfare by supporting research and education in all fields of science and engineering."

NSF funds research and education in most fields of science and engineering. It does this through grants and cooperative agreements to more than 2,000 colleges, universities, K-12 school systems, businesses, informal science organizations and other research organizations throughout the US. The Foundation accounts for about one-fourth of Federal support to academic institutions for basic research.

NSF receives approximately 55,000 proposals each year for research, education and training projects, of which approximately 11,000 are funded. In addition, the Foundation receives several thousand applications for graduate and postdoctoral fellowships. The agency operates no laboratories itself but does support National Research Centers, user facilities, certain oceanographic vessels and Arctic and Antarctic research stations. The Foundation also supports cooperative research between universities and industry, US participation in international scientific and engineering efforts, and educational activities at every academic level.

Facilitation Awards for Scientists and Engineers with Disabilities (FASED) provide funding for special assistance or equipment to enable persons with disabilities to work on NSF-supported projects. See the NSF Proposal & Award Policies & Procedures Guide Chapter II.F.7 for instructions regarding preparation of these types of proposals.

The National Science Foundation has Telephonic Device for the Deaf (TDD) and Federal Information Relay Service (FIRS) capabilities that enable individuals with hearing impairments to communicate with the Foundation about NSF programs, employment or general information. TDD may be accessed at (703) 292-5090 and (800) 281-8749, FIRS at (800) 877-8339.

The National Science Foundation Information Center may be reached at (703) 292-5111.

The National Science Foundation promotes and advances scientific progress in the United States by competitively awarding grants and cooperative agreements for research and education in the sciences, mathematics, and engineering. To get the latest information about program deadlines, to download copies of NSF publications, and to access abstracts of awards, visit the NSF Website at 2415 Eisenhower Avenue, Alexandria, VA 22314 (NSF Information Center) (703) 292-5111 (703) 292-5090 Send an e-mail to: or telephone: (703) 292-8134 (703) 292-5111 Privacy Act And Public Burden Statements The information requested on proposal forms and project reports is solicited under the authority of the National Science Foundation Act of 1950, as amended. The information on proposal forms will be used in connection with the selection of qualified proposals; and project reports submitted by proposers will be used for program evaluation and reporting within the Executive Branch and to Congress. The information requested may be disclosed to qualified reviewers and staff assistants as part of the proposal review process; to proposer institutions/grantees to provide or obtain data regarding the proposal review process, award decisions, or the administration of awards; to government contractors, experts, volunteers and researchers and educators as necessary to complete assigned work; to other government agencies or other entities needing information regarding proposers or nominees as part of a joint application review process, or in order to coordinate programs or policy; and to another Federal agency, court, or party in a court or Federal administrative proceeding if the government is a party. Information about Principal Investigators may be added to the Reviewer file and used to select potential candidates to serve as peer reviewers or advisory committee members. See System of Record Notices , NSF-50 , "Principal Investigator/Proposal File and Associated Records," and NSF-51 , "Reviewer/Proposal File and Associated Records.” Submission of the information is voluntary. Failure to provide full and complete information, however, may reduce the possibility of receiving an award. An agency may not conduct or sponsor, and a person is not required to respond to, an information collection unless it displays a valid Office of Management and Budget (OMB) control number. The OMB control number for this collection is 3145-0058. Public reporting burden for this collection of information is estimated to average 120 hours per response, including the time for reviewing instructions. Send comments regarding the burden estimate and any other aspect of this collection of information, including suggestions for reducing this burden, to: Suzanne H. Plimpton Reports Clearance Officer Policy Office, Division of Institution and Award Support Office of Budget, Finance, and Award Management National Science Foundation Alexandria, VA 22314 Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. View all journals Explore content About the journal Publish with us Sign up for alerts Open access Published: 12 September 2024 The global geography of artificial intelligence in life science research Leo Schmallenbach   ORCID: orcid.org/0000-0001-5302-2578 1 , Till W. Bärnighausen 2 , 3 , 4 & Marc J. Lerchenmueller   ORCID: orcid.org/0000-0001-8875-3602 1 , 5   Nature Communications volume  15 , Article number:  7527 ( 2024 ) Cite this article 2 Altmetric Metrics details Machine learning Translational research Artificial intelligence (AI) promises to transform medicine, but the geographic concentration of AI expertize may hinder its equitable application. We analyze 397,967 AI life science research publications from 2000 to 2022 and 14.5 million associated citations, creating a global atlas that distinguishes productivity (i.e., publications), quality-adjusted productivity (i.e., publications stratified by field-normalized rankings of publishing outlets), and relevance (i.e., citations). While Asia leads in total publications, Northern America and Europe contribute most of the AI research appearing in high-ranking outlets, generating up to 50% more citations than other regions. At the global level, international collaborations produce more impactful research, but have stagnated relative to national research efforts. Our findings suggest that greater integration of global expertize could help AI deliver on its promise and contribute to better global health. Similar content being viewed by others Reporting guidelines in medical artificial intelligence: a systematic review and meta-analysis The potential for artificial intelligence to transform healthcare: perspectives from international health leaders Guiding principles for the responsible development of artificial intelligence tools for healthcare, introduction. Artificial intelligence (AI) promises to transform the life sciences and, ultimately, medical care 1 . Broadly defined, AI refers to the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings 2 . In the life sciences, AI is already widely used, for example, when computers analyze large amounts of patient data to aid in initial diagnoses, or when algorithms optimize patient enrollment in clinical trials for drug development 3 , 4 , 5 . The high hopes for the growing use of AI technology are reflected in estimates that the global market for AI-based medical care will grow eightfold by 2027 6 . Against this backdrop, the geography of the AI life science research enterprise, i.e., research that incorporates AI in a life science context, is important for at least three reasons. First, a longstanding line of research has documented that scientific advancement benefits from collaboration 7 , especially across borders 8 . Research ideas are rarely confined to national boundaries, the talent needed to conduct research is geographically dispersed, and the challenges of a globalized world require the collaboration of international scientists to derive integrated insights 9 , 10 . Second, and more specific to AI in the life sciences, geographically concentrated research runs the risk of creating biased data foundations that distort inferences and, possibly, lead to biased medical care 11 . Recent research has already documented biases showing, for example, that the underrepresentation of ethnicities in training data can lead to distortions in prognosis, diagnosis, and treatment 12 , 13 . As the AI research agenda in the life sciences rapidly accelerates, fueled by national funding and possibly concomitant interests, questions about effectiveness and equity have grown. Third, AI applications in healthcare promise to deliver high-quality medical care without relying on the expensive and complex machinery traditionally required 14 , 15 . AI-driven diagnostics and treatment plans can be implemented using more accessible and affordable technologies, such as smartphones and simple medical devices. Such democratization of healthcare technology could enable remote and underserved regions to access advanced medical care that was previously out of reach. These regions must, however, partake in AI-powered life science research to ensure that newly developed technologies meet local needs and to build the capabilities and trust needed for application. In short, the geography of AI research matters for harnessing AI’s promises to the benefit of global patient populations. Existing studies on the geography of AI research, both across scientific disciplines and specific to the life sciences, describe a geographically concentrated enterprise. Studies have shown that China and the United States (US) have come to dominate the AI research system in terms of funding, active scientists and, consequentially, the number of publications 16 , 17 , 18 . A recent meta-analysis at the intersection of general and healthcare-specific AI, which reviewed 288 studies across the disciplines of accounting and management, decision sciences, and health professions, documented a rapidly growing body of AI research, with the US and China contributing the most publications 19 . The study that, to our knowledge, comes closest to our focus on the life sciences, analyzed 3529 scientific AI publications between 2000 and 2021, and again found the US and China to be the most productive geographies based on the number of publications 20 . We provide a summary of our literature review in the Supplementary Material (S 1) . We extend this emerging and productivity-focused line of research by analyzing the geography of AI research in the life sciences using three dimensions: Productivity, i.e., publication counts at the country level as well as at the level of world regions, with additional stratification of publications by field of AI application. Quality-adjusted productivity, i.e., publications stratified by field-normalized quality rankings of the publishing outlets. Relevance, i.e., forward citations received by a focal piece of research, additionally stratifying citations into accruing from general research and clinical research. We apply the three dimensions to a sample of 397,967 AI life science publications and 14.5 million associated citations, creating a multidimensional global atlas spanning over two decades of research (2000–2022). A detailed sampling protocol, variable descriptions, econometric techniques, and sensitivity analyses are outlined in the Methods. In brief, we use keyword-based text mining and machine learning techniques to identify and classify AI research at the intersection of the life sciences and computer science. We use the standard bibliographic reference for life science research, the PubMed database, to retrieve 374,501 AI-relevant publications from life science journals. To cover computer science, we use the OpenAlex database with its comprehensive indexing and identify 23,466 AI-relevant conference proceedings publications with a life science focus. For constructing our global atlas of AI life science research, we pool the datasets and henceforth use the terms “articles” or “publications” to refer to both journal and conference proceedings publications. To proxy the accuracy of our identification approach, we manually inspect a random sample of 300 articles for AI and life science relevance and test our obtained article coverage against a set of AI special issues in life science journals, obtaining corroborating results. We then stratify the obtained 397,967 AI life science publications by the country of affiliation of the lead author of the articles, i.e., the last author where available, and the first author otherwise, reflecting common authorship norms 21 , 22 . For the first dimension of our atlas, we analyze the geography of production both at the country level as well as at the level of world regions, according to the six world regions defined by the United Nations: Africa, Asia, Europe, Latin America, Northern America, and Oceania 23 . We also stratify productivity by field of AI life science application, employing the OpenAlex content classification algorithm and keyword-based identification of clinical research. To assess the second dimension, we adjust productivity with a field-normalized approximation for quality, distinguishing articles published in the top three ranked journals and conference proceedings publications for a given field. Finally, we assess the relevance of published research by linking 14.5 million forward citations, distinguishing citations arising from general versus clinical research. To analyze the geography of the first two dimensions (productivity and quality-adjusted productivity), we use descriptive data visualization. To assess the geographic variance in the relevance of the research produced, we use negative binomial regression models. This class of models can accurately estimate the influence of geography, content, and quality of research on relevance (i.e., citations) by also accounting for the skewed distributional properties of citations as the dependent variable. The three-dimensional assessment provides a nuanced geography of the AI life science research enterprise. Asia leads the global production of AI life science research in absolute terms, with China accounting for over 50% of the region’s publications. Examining the content of publications reveals that many countries contribute to core AI research areas in the life sciences (dimension 1). When productivity is adjusted for quality (dimension 2), the regions of Northern America and Europe contribute most publications in high-ranking outlets. We also find that the dimensions of quality and relevance are strongly correlated, with research from Northern America and Europe receiving a substantial citation premium relative to other regions (dimension 3). This citation premium appears to be mostly explained by our approximation of underlying research quality. We complement the three-dimensional assessment of geography by examining international (versus national) collaborations, defined as articles with at least two authors on the author byline who are affiliated with different countries. We present evidence for greater relevance of research conducted in international collaborations as opposed to national collaborations. Despite creating research of greater relevance, the share of international collaborations stagnates and the propensity to collaborate internationally differs between world regions. Dimension one: productivity We begin our assessment of productivity by documenting an exponential increase in global AI life science publications (Fig.  1 ), quantified by a 20% annual growth rate since 2010. Yearly counts of articles ( n  = 397,967) with AI-related keywords in titles or abstracts from 2000 to 2022. Growth refers to the compound annual growth rate (CAGR 2010–2022). Source data are provided as a Source Data file. Continuing with the first dimension of our atlas, we show a geographical concentration of AI life science research in the US (101,195 articles), followed by China (73,129 articles), together accounting for about 44% of cumulative productivity between 2000-2022 (Fig.  2 ). Of note, 2020 marks the first year in which China has surpassed the US in the number of publications per year in our dataset (see dynamic online graph for details). In terms of cumulative productivity, there is a marked gap between the US, China, and the next tier of countries, which is led by the United Kingdom (21,215 articles), Germany (18,759 articles), Japan (15,263), Canada (12,578 articles), India (12,560 articles), and South Korea (12,264 articles). Select countries, like India, show differences between their productivity in life science journal publications versus computer science conference publications with a life science focus. We provide a table showing all countries’ individual productivity statistics in the Supplementary Material S 2 . While the regions of Asia, Europe, Northern America, and Oceania all tangibly contribute research, countries in Africa and Latin America show moderate-to-low involvement in the AI life science research enterprise. These data underscore two concerns: An almost bipolar geographic concentration of AI research productivity, led by the US and China, while countries from Africa and Latin America remain little involved in AI life science research. Counts of AI-focused life science articles by country, cumulated for the years 2000 to 2022 ( n  = 397,967). Source data are provided as a Source Data file. We next consider whether the observed geographic concentration goes in hand with a concentration in research topics and underlying capabilities, which may cater to productivity advantages of some countries over others. In a first step, we assign articles to content categories available from the OpenAlex database. We provide further details on the categorization in the Methods and in the Supplementary Material (S 3 ). We focus our analysis on the 40 most frequent content categories in our dataset, representing, on average, two-thirds of AI life science research across the 40 most productive countries. These 40 countries collectively account for 96% of global productivity in our data. To examine the resulting content-by-country (40 × 40) data matrix, we create a heatmap visualization in Fig.  3 . The individual cells of the heatmap contain the share of a country’s publications for a specific content category relative to all publications by the same country. This share, expressing nations’ research foci, also defines the heatmap’s color, with darker shading representing less focus and lighter shading greater focus. The heatmap first indicates that there are many fields that yet stand to gain from further AI applications, indicated by the broad space covered by darker coloring across world regions. Looking at the most productive AI life science research categories, such as computer vision, computational biology, neuroscience, internal medicine, statistics, radiology, and surgery, there is a global focus rather than geographic specialization. Thus, topic specialization does not appear to be driving the concentration of productivity visible in Fig.  2 . The horizontal axis enlists the 40 most productive countries grouped by geographic region. The vertical axis depicts the underlying publication topics in descending order (computer vision being the most frequently researched topic). The color scheme of the heatmap reflects the percentage share of country-specific productivity for a given publication topic ( n  = 397,967). Source data are provided as a Source Data file. Extending our productivity stratification for content, we assess the extent to which countries generally conduct clinical research with the application of AI. Clinical research is of particular interest because it reflects research with potential applications that more directly benefit human health. To identify clinical research, we rely on a search strategy proposed by Haynes and colleagues 24 , 25 , further described in the Methods. Overall, AI-focused clinical research accounts for about 20% of the articles included in our sample. Figure  4 depicts the geographic distribution across the 30 most productive countries together accounting for 94% of global production of clinical AI research. The primary vertical axis shows the share of a country’s clinical research articles relative to all clinical research articles globally (blue bars), while the secondary vertical axis shows the share of a country’s clinical research articles relative to all AI life science articles published by that country (orange bars). Comparable to general productivity, we observe the US and China account for about 45% of AI clinical research, with several countries from all world regions, except for Africa and Latin America, contributing tangibly to the clinical AI research enterprise. Consistent with the content analysis presented in Fig.  3 , we also find that many countries devote 15–20% of their research efforts to AI clinical research. The share of a country’s clinical research relative to global clinical research production (primary y -axis) and relative to all publications within the same country (secondary y -axis) for the 30 most productive countries in terms of clinical articles ( n  = 67,167). Source data are provided as a Source Data file. Dimension two: quality-adjusted productivity Next, we examine whether the geographic concentration we observe in the number of publications is accompanied by a concentration in quality. Scientific progress tends to be driven by research of unusual rather than average quality 26 , 27 , traditionally motivating dedicated examinations of the right-hand tail of the research quality distribution 28 . To adjust for quality with a field-normalized approach, we use external rankings of journals and conferences. For journal articles, we consider articles published in one of the top three journals within a given journal category according to Clarivate’s Journal Citation Report. For conference proceedings, we consider articles published in a proceedings publication of conferences ranked “A*”, according to the CORE conference ranking 29 . For journal publications, this approach classifies about 8% of the research as appearing in high quality outlets, and for conference proceedings publications about 6% (S4). We find that the US, Australia, and several European countries contribute the largest shares of research in high-quality outlets over the period 2000–2022 (Fig.  5 ). Compared to general productivity, China, and other Asian countries, as well as countries in Latin America rank in the midfield towards the lower-end of the quality-adjusted productivity distribution. Africa, meanwhile, remains largely absent from this mapping due to overall low productivity, including in top-ranked outlets. A notable exception is Kenya, which has international collaborators on two-thirds of its publications placed in high-ranking outlets, while, for example, one-third of South African publications have international collaborators. We discuss the role of internationally collaborated research in a separate section below. Percentage shares of AI-focused life science articles published in high-ranked outlets by country, cumulated for the years 2000 to 2022 ( n  = 31,837). The analysis is limited to countries with at least 100 publications. Source data are provided as a Source Data file. Moving the analysis from the country level to the level of world regions, we seek to examine the consistency with which regions can contribute to AI-focused life science research published in high-ranking outlets. Figure  6 depicts relatively stable proportions of research that distinguish into two groups of regions. On the one hand, there is the group of Northern America, Europe, and Oceania that places consistently about 10% of their published research in high-ranking outlets. On the other hand, there is a group consisting of Asia, Latin America, and Africa, who publish about 5% of papers in these top-ranked outlets. Europe and Asia have shown opposite trends in recent years, with Europe gradually decreasing and Asia gradually increasing their respective shares of publications in high-quality outlets. Percentage shares of AI life science articles published in high-ranked outlets by geographic region and per year ( n  = 32,010). Source data are provided as a Source Data file. Dimension three: relevance To assess the third dimension of the atlas, we examine geographic variance in the relevance of the produced research. We conceptualize relevance as the extent to which focal publications inform (a) scientific progress (scientific relevance) and (b) clinical application (clinical relevance). We operationalize relevance via forward citations to the AI life science articles in our sample. As econometric model, we employ negative binomial regression models to account for the overdispersion of citation measures. We regress citation counts on dummy variables representing the six geographic regions, setting the most productive region, Asia, as the base category. We control for the publication year to account for the time a given article had to accrue citations. Figure  7 shows incidence rate ratios (IRRs) obtained from the negative binomial regression models. These ratios can be interpreted as percentage changes in the dependent variable, citations, given a one-unit change in the independent dummy variables, i.e., given the geography of the focal articles across the six world regions. All panels depict incidence rate ratios (IRRs) with error bars for 95% confidence intervals obtained from negative binomial regressions of citations on dummy variables for the geography of the research, with the most productive region, Asia, serving as the base category. A ( n  = 397,965) and B ( n  = 397,965) show unadjusted estimates (only accounting for publication year), whereas ( C ) ( n  = 393,722) and ( D ) ( n  = 375,033) also include controls for quality variation across publishing outlets. As publishing outlets with all zero outcomes for the dependent variable (i.e., publishing research that is not cited) get automatically dropped from the analyses with quality controls, the sample sizes are smaller in ( C and D ). Source data are provided as a Source Data file. Scientific relevance We assess an article’s scientific relevance as the number of forward citations an article receives from general life science research articles. We find that AI-focused life science research produced in the world regions of Africa, Oceania, Europe and Northern America receives about 10% (95% confidence interval (CI) 6%–15%), 26% (95% CI 23%–29%), 20% (95% CI 19%–22%), and 40% (95% CI 38%–42%) more forward citations in general life science articles, respectively, than research created in Asia (Fig.  7A ). Research produced in Latin America, in comparison, receives fewer forward citations than research from Asia. To adjust for the quality of the underlying research, we next include dummy variables for each journal and conference proceedings outlet in our regression model (i.e., outlet fixed effects). The inclusion of fixed effects adjusts for any geographical variance in research tied to the outlet, including quality ranking and subject matter published. In this adjusted model, with the exception of Africa and Latin America, world regions are no longer statistically different in terms of forward citations in downstream life sciences research (Fig.  7D ). In other words, the citation differences between geographic regions appear to be largely explained by regional differences in research quality, which is consistent with the geographic variance in research quality shown in Fig.  5 . Clinical relevance Ultimately, AI is expected to transform medicine. We therefore seek to analyze the influence of AI life science research on clinically applied research. Figure  7B shows that the regions of Oceania, Europe and Northern America receive a citation premium from downstream clinical research articles (about 13% (95% CI 8%–17%), 26% (95% CI 24%–29%), and 55% (95% CI 52%–57%) respectively), compared to research generated in Asia, analogous to the scientific relevance dimension (Fig.  7A ). The greater number of clinical citations to AI life science articles from these three regions again appears to be explained by our approximation of the underlying research quality (Fig.  7C ). Overall, the findings in Fig.  7 indicate that the differences in scientific and clinical relevance are driven by differences in quality rather than geographic bias in citation patterns. In other words, the cumulative knowledge-building process in the AI research enterprise appears to be largely unbiased with respect to the geographic location of the knowledge-creating researchers. International collaborations Lastly, we return to the argument that scientific progress is driven by collaborating on the best ideas, irrespective of the ideas’ geography. We analyze international collaborations in our dataset and define articles as international if at least two authors on the author byline are affiliated with institutions from different countries. We focus this analysis on the relevance dimension, because it is the best proxy for what kind of research informs the advancement of the global research enterprise. We again estimate negative binomial regression models with citations as dependent variables and a dummy variable for international collaboration as the core independent variable. In the analysis of a potential citation differential between research from international versus national collaborations, we control for three factors. We include dummy variables for the lead author country to account for regional variance in international collaboration. We control for the number of co-authors because larger author teams are more likely to include a co-author from another country and team size has been shown to correlate with citations 7 . Additionally, we control for the publication year of a focal article to account for the time it had to accrue citations. We find that articles stemming from international rather than national collaborations receive, on average, 21% (95% CI 20%–22%) more citations by general life science articles and 7% (95% CI 6%–8%) more citations by clinical life science articles (Fig.  8A ). Of note, international collaborations also tend to publish 35% more frequently in high-ranking research outlets than national collaborations, on average. The effect of international collaboration on scientific and clinical relevance ( A ); share of international collaborations over time ( B ); share of international collaborations by region ( C ). Incidence rate ratios (IRRs) with error bars for 95% confidence intervals obtained from negative binomial regressions of citations ( n  = 397,949) and clinical citations ( n  = 397,887) on a dummy variable for international collaboration, accounting for country of lead author, team size, and publication year ( A ). Percentage share of articles with at least two authors affiliated in different countries ( n  = 397,965) ( B ). Percentage share of articles with at least two authors affiliated in different countries by geographic region ( n  = 397,965) ( C ). Source data are provided as a Source Data file. Despite apparent benefits of collaborating across borders, the share of internationally collaborated research is with less than 20% over time relatively low and has come to stagnate in proportion (Fig.  8B ). However, the extent to which regions engage in international collaboration varies. Figure  8C shows the share of publications that stem from international collaboration by region of the lead author. While African lead authors coauthor 36% of their publications with at least one collaborator from a different country, Asian lead authors do so for only 16% of their articles. Oceania (32%), Europe (27%), and Latin America (23%) range in between, whereas Northern America also tends to emphasize national over international collaborations (18%). To further contextualize this cross-regional variance in international collaborations, our final analysis characterizes the dyadic relationships between regions that engage in international collaborations. Figure  9 presents an alluvial diagram to show patterns of international collaboration, including, by construction, only the articles identified as international. We count each occurrence of a difference in geographic location separately and sum international collaborations to the regional level. In other words, if a lead author’s country of affiliation is the US and the lead author collaborates with co-authors from China and Germany, then we depict two lines in the alluvial diagram, one from Northern America to Asia and one from Northern America to Europe. The vertical bars on the left depict the sum of outgoing international collaborations from lead authors affiliated in the respective region, while the right vertical bars depict the incoming collaborations for non-lead authors from the respective region. Number of dyadic collaborations between authors from different countries, aggregated to the regional level. Dyadic collaborations are counted as co-authorships between a publication’s lead author (last author or first author otherwise) and any other author on the author byline that is from a different country. Only international dyads are considered ( n  = 105,258 dyads). Source data are provided as a Source Data file. Overall, Fig.  9 shows that Europe engages most frequently in international collaborations, both from an outgoing perspective (lead authors) as well as an incoming perspective (other authors), represented in the blue vertical bars on both sides of the diagram. European researchers most frequently collaborate with colleagues from the same geographic region, followed by Northern America. But Europe also appears to play an important role in partnering with African and Latin American researchers. Oceania’s international collaborations appear most pronounced with Asia. Africa collaborates frequently with European and Asian researchers. Latin America appears more varied in its international collaboration patterns, but also appears collaborating most frequently with researchers based in Europe. Northern American lead authors tend to mostly co-author with colleagues from the same region, followed by collaborations with Asia and Europe. Prior research with a focus on productivity has identified the US and China as the main contributors of AI research. In this study, we first test whether this general finding holds true in the specific context of AI applications in life science research and document that the US and China produced almost half of the global AI life science research between 2000 and 2022. Taking a regional perspective, Asia leads global production. We then extend this one-dimensional perspective on research output by considering two additional dimensions: quality-adjusted productivity and relevance. We show that the geography of global AI life science research changes depending on the dimension under consideration. For example, we show that the world regions of Northern America and Europe produce most life science articles published in high-ranking outlets and, alongside Oceania, produce work that most advances the AI life science research enterprise. Meanwhile, the world regions of Latin America and Africa markedly lag as contributors to the AI life science research enterprise. We show that exceptions to this pattern, like select African countries, disproportionally engage in international collaborations to produce research in high-ranking outlets that is also of high relevance. The productivity-focused analysis of AI research in prior literature has contributed to concerns about national research agendas potentially undermining the effective and equitable advancement of AI research across science fields. In the wake of rising nationalism and protectionism, researchers have come to summarize these bipolar geographic dynamics as a China–US “arms race” in AI 30 . The public discourse feeds this conception. For example, in 2017, China announced a program for the domestic development of AI with the objective of becoming the world’s leading AI region by 2030 and has recently underscored its ambitions by pledging a record investment in AI-enabling infrastructure 31 . As AI research uniquely requires large-scale investments, including scaled computing resources, trained human capital, and encompassing data, China successfully accelerates its AI research program 32 . These geopolitical dynamics invigorate the “arms race” perspective on AI research, which is to be appreciated in light of evidence that governments’ AI investment can also be politically motivated 33 . Our assessment of productivity mirrors the US-China duopoly perspective also for the life sciences, and it remains subject to further research on how this geographic concentration in productivity influences the advancement of AI in the life sciences and elsewhere longer term. However, considering quality-adjusted productivity and relevance as two further dimensions of evaluation provides additional, different perspectives. Other world regions, home to many countries, produce AI life science research that is disproportionally used in advancing the AI research enterprise. Research from the regions of Europe, Oceania, and Northern America gets cited at 1.2 to 1.5 times higher rates in general and clinical research when compared to other world regions. This citation premium appears explained by the quality of the underlying research. Some of this quality stratification proxied by the field-specific ranking of the outlets publishing the research may stem from differential access to journals or conferences across geographies. Not all researchers may be equally comfortable publishing their findings in English, generally the standard language of academic communication in international journals, for example. Still, journal and conference proceedings publications remain the central avenue for cumulative knowledge building in the sciences 34 . Overall, our results show that the regions of Northern America, Oceania, and Europe are key regions producing relevant research to advance the AI life science research enterprise. Geographic differences in research quantity and quality may further hold implications for how scholars model the evolution of the AI research enterprise more generally. Studies predicated on quantitative productivity have mostly equated investments in inputs with superior outputs. For example, cities able to attract the largest number of AI scientists have been found to emerge as the cities accomplishing the largest number of AI publications 16 . Similarly, national research funding has been correlated with publication output 17 . These findings notwithstanding, our study argues and shows that output can be conceptualized along multiple dimensions. While countries in our study each individually operate with a given set of inputs for producing AI research, there is considerable variance in output when comparing quantity to quality and ensuing relevance. As such, we submit that further research is needed that examines the input-output relationship in AI research, in the life sciences and possibly other disciplines, to better understand research trajectories. Beyond different types of geographic concentrations, we find that international collaborations produce more relevant research than national collaborations. Consistent with previous research highlighting the importance of scientists collaborating across borders 8 , 9 , 10 , we find a citation premium of more than 20% for international versus national collaborations, specifically in the AI life sciences context. Despite this apparent importance of internationally conducted AI research for cumulative knowledge building in the life sciences, the rate at which scientists collaborate internationally appears to stagnate. Exceptions to this pattern emerge in select countries that successfully use international collaborations to disproportionally produce research in high-ranking outlets and of high relevance. While the average rate of international collaborations hovers around 20% in our data, countries like Kenya collaborate internationally on over 40% of their publications and even on two-thirds of the publications placed in high-ranked outlets. International collaborations may thus prove instrumental for broadening geographic participation in the AI life science research enterprise. We also find, however, that the proclivity to international collaboration varies. The most productive world regions of Northern America and Asia team across borders at the lowest rates, while scientists located in Europe collaborate internationally at higher rates and more geographically distributed. Europe collaborating internationally may thus particularly cross-pollinate research conducted in the world regions of Africa and Latin America, which overall create a tangible share of their productivity through international collaborations. Lastly and importantly, our global atlas shows many world regions remain moderately or little involved in the AI life science research enterprise. Countries in Africa and Latin America account for less than 5% of global AI research in the life sciences. These two world regions are home to more than 25% of the world population and experience more than half of the global disease burden 35 . That is not to say that the existing research does not tackle research questions that are also germane to these regions. In fact, the possibility of scaling AI applications across world regions may lead to marked benefits for many countries, even if countries are not all involved to a similar extent in the creation of the research. Still, our findings add to a concern that may be especially applicable to the life sciences. The prowess of AI often depends on the data foundation fed to learning-based models. If research remains geographically concentrated, it stands to reason that data foundations evolve in an unbalanced fashion. In turn, the imbalance could lead to biased AI models producing biased recommendations. Patient populations are diverse in terms of gender, race, and ethnicity, as well as other attributes, like socio-demographic status or access to healthcare systems. To mitigate the risk of AI informed medical care being biased towards certain demographics, straddling these different characteristics requires more and accelerated research and building the necessary capabilities and training datasets globally. Parts of the life science community have voiced these concerns, and studies have begun to selectively expose such biases 36 . Our global atlas may be viewed as underscoring the geographic magnitude of these concerns and points to examining the desirability and design of potential countermeasures. Our study is not without limitations. First, we use the affiliation country of the lead author (last author where available and first author otherwise) to determine the geography of a focal article. Although this approach is in line with characterizing research according to the characteristics of lead authors 21 , 37 , it still focuses on the academic creators of research. Future work may enhance the global atlas of AI life science research by, for example, considering the location of supporting funding institutions or the geography of academy-industry collaborations. Second, we rely on a keyword-based identification of clinical research to distinguish the nature of forward citations. Future research examining the nature and detailed content of clinical studies seems warranted. For example, scholars might more qualitatively examine the kind of clinical research that draws on AI techniques, as well as characterize the medical fields most poised to benefit clinically from AI. Finally, the goal of our study is to provide an atlas of AI life science research. As a corollary, we focused on the “supply side” of research. Bridging this supply-side perspective to a demand-side perspective seems a fruitful research area, addressing questions like what patient populations stand to gain (or lose) from AI advances. Third, our research, which covers AI applications in the life sciences through 2022, largely misses the very recent surge in studies using large language models (LLMs). These models are poised to have a major impact on a range of biomedical applications, from synthesizing expert literature to improving patient communication and medical education. The rapid development and integration of LLMs highlight the need for ongoing research to better understand their capabilities and ensure equitable benefits. Consideration of the geography of LLM applications is likely critical to addressing access disparities, understanding local implementation challenges, and promoting global health equity. In conclusion, our study offers a global atlas of AI life science research published between 2000–2022 along three dimensions: productivity, quality-adjusted productivity, and relevance. We show that geographic gravity changes across these dimensions. Overall, the productivity dimension shows Northern America and Asia to dominate, led by the US and China respectively. By contrast, the world regions of Northern America, Oceania, and Europe, with several countries contributing to publications in high-quality outlets, produce research most relevant for advancing the AI life science enterprise. The world regions of Latin America and Africa remain largely absent from the global atlas of AI life science research. Beyond this differentiating geographical view, we show that integrating international collaborations is instrumental for the creation of relevant research. Yet, the internationality of the AI life science research enterprise stagnates. To best advance AI research, concerted international efforts may be warranted. To identify AI-focused life science articles, we take a two-pronged approach that reflects the interdisciplinary nature of this research. On the one hand, we start from the life sciences perspective, turning to the PubMed XML database as the world’s most comprehensive inventory of biomedical literature, with more than 35 million articles linked to a range of supporting information. On the other hand, we start from the computer science perspective, turning to articles published in conference proceedings indexed in OpenAlex, a database successor of Microsoft Academic Graph (MAG), containing detailed bibliometric information on more than 250 million scholarly works. Recent research documents OpenAlex to have the widest coverage of academic publications, especially for non-journal publications 38 . We adopt the search strategy established by Baruffaldi et al. 29 , which relies on an encompassing keyword search for articles containing AI terms in their title or abstract, and we apply the approach to both data foundations. Baruffaldi and colleagues followed a three-step approach to create a set of query terms for bibliometric databases that accurately retrieve documents focused on AI. In the first step, the authors identified articles published in AI-tagged journals and conference proceedings according to the All Science Journal Classification (ASJC). In the next step, the authors identified keywords listed in these documents and performed a co-occurrence analysis of these keywords based on the titles and abstracts of the AI-tagged documents. Only keywords that appeared at least 100 times and belonged to the top 60% in terms of relevance were kept. Finally, this list of keywords was presented to and approved by a group of AI experts from academia and industry. We provide the final list of 214 AI-related keywords in the Supplementary Material (S 5) . For our identification of AI-focused life science research indexed in PubMed, we use these 214 keywords to identify publications containing any of them in either the title or abstract of articles published between 2000 and 2022. In total, we identified 388,633 AI-related life science articles indexed in PubMed. To identify AI-focused life science research in conference proceedings, we first searched for the 214 AI keywords in the title and abstract of 2.4 million documents linked to all 10,794 conferences listed in OpenAlex. In the next step, we apply the content classification embedded in the OpenAlex database to identify AI research that also addresses concepts relevant to the life sciences. OpenAlex tags articles with multiple concepts representing their topical focus, using an automated state-of-the-art machine learning classifier based on titles and abstracts with confidence scores indicating relevance 39 . These scientific concepts are organized hierarchically, with 19 root-level concepts branching into six levels of specific topics. When a lower-level concept is mapped, all its parent concepts are mapped as well, ensuring comprehensive coverage 39 , 40 . We consider articles that have been assigned at least one of the following four top-level concepts (defined as level 0 in Open Alex) related to life science research: Biology, Chemistry, Medicine, or Psychology. We cross-verify the representativeness of these terms for life science research in our sample of PubMed articles. Here, these four terms represent more than 80% of the indexed life science research. This approach gives us 28,848 conference proceedings publications at the intersection of AI and the life sciences. We evaluate the accuracy of our approach, summarize the results here, and provide further details in the Supplementary Material (S 6) . To test precision, we examine a random sample of 150 PubMed articles and 150 conference articles from our final dataset, employing two independent reviewers to rate the PubMed articles for AI focus and the conference articles for life science relevance. In an iterative process, 90% of the PubMed articles were designated as containing a certain type of AI application and 93% of the conference articles as having a life science focus. In addition, we evaluate the comprehensiveness of our approach in identifying AI life science research by looking at the coverage of articles published in AI special issues of life science journals. In a sample of 15 special issues published in 2022, we find that 92% of the articles published in these special issues are also part of our sample. We also compare the chosen search strategy with a second search strategy for AI-related publications proposed by Liu and colleagues 41 . We find that while the Liu et al. approach is slightly more precise, the approach proposed by Baruffaldi and colleagues yields more than twice as many articles, making it more comprehensive. We map the 417,481 articles identified with the above search approach to countries using the affiliation of the lead author as recorded in OpenAlex, if available, and otherwise as recorded in PubMed. To link the corpus of PubMed articles to OpenAlex, we leverage unique article identifiers, i.e., PubMed IDs (PMID) and/or Digital Object Identifiers (DOI), and query the OpenAlex application programming interface. To identify the lead author of an article, we use long-established authorship norms 21 , 22 , that reserve the first and last author positions for the lead authors of an article. Since the last author is usually the more senior author, who typically sponsors the necessary research infrastructure (e.g., laboratory and office space), we designate the geographic location of an article based on the country of affiliation associated with the last author when available. Otherwise, we use the affiliation information for the first author. For approximately 90% of the articles in our sample, the affiliation of the first and last author is linked to the same country. We identify a country for 397,967 (95%) articles that make up the final sample of our analysis. We assess the reliability of our country assignment for a random sample of 300 articles with the help of two independent raters and obtain a correct country assignment for 99% of the observations. Using our final sample, we then map the individual countries to geographic regions according to the United Nations classification: Africa, Asia, Europe, Latin America (including the Caribbean), Northern America, and Oceania 23 . We also collect information on the affiliation of interior authors and use their location to identify international collaborations, which we define as articles where at least one author is affiliated with a country that is different from that of the lead author. Figure  10 summarizes the steps of our sample creation approach. Overview of sample creation using PubMed and OpenAlex as main databases. To enrich our data with information about the content of individual articles, we again make use of the concepts provided in the OpenAlex database. Specifically, we assign each article to the level 1 concepts with the highest relevance score. If this highest scoring concept is a purely AI-related term, we assign the next highest scoring concept to ensure that the assigned concept reflects the context of life science applications. In addition, we create a subset of articles related to clinical research by performing a keyword search for clinical keywords in the title or abstract 24 , 25 . We expand the dataset for our quality-adjusted productivity analysis with journal-level information from Clarivate’s Journal Citation Report (JCR 2020). We link our data to this report using the unique International Standard Serial Number (ISSN) of the publishing journal. In total, 328,062 (88%) articles of our PubMed corpus were published in a journal indexed by the JCR. Of relevance to our analysis is the journal’s rank within the same journal category based on the journal’s impact factor. The Pearson correlation between journal impact factors from different vintages of the JCR is generally greater than 0.9, indicating little temporal variance in the scaling of the metric 37 . We consider any publication published in one of the three highest-ranked journals within the same journal category. Because journals can be assigned to multiple categories, we consider the journal category in which a focal journal ranks highest. PubMed articles not published in indexed journals were not considered as Clarivate sets a quality threshold for journal inclusion in the index. Using this approach, we identify 29,510 (8%) articles in our PubMed corpus as being published in high-ranked journals. We further identify high-quality conference proceedings by making use of an external conference ranking, the so-called CORE ranking, provided by the Computing Research and Education Association of Australasia. CORE provides expert-based assessments of all major conferences in the computing disciplines with information on their research subfield and is a standard resource for ranking computer science conferences 29 . We consider all publications in A*-rated conference proceedings to be of high quality, resulting in 1349 articles (6% of our total sample). We differentiate our analyses between life science and computer science articles in S7. To characterize the scientific and clinical relevance of an article, we leverage detailed forward citation data from OpenAlex. The database provides not only detailed bibliometrics and metadata but also more than 1 billion citation links between publications. We identify these citation links for the articles in our sample by their PMIDs, DOIs, and OpenAlex IDs. We further distinguish citations into those accruing from any type of publication and those from clinical research only. For this distinction, we again use the keyword-based approach of Haynes and colleagues to identify clinical research 24 , 25 . We analyze the geography of AI life science research according to the defined three dimensions, namely productivity, quality-adjusted productivity, and relevance, employing data visualization and regression models. More specifically, we visualize descriptive statistics, including publication counts (Figs.  1 and 2 ) and geographic percentage shares stratified by content and quality of underlying research (Figs.  3 – 6 ). To gauge the scientific and clinical relevance of AI life science research by geographic region, we use negative binomial regression models with the number of citations as the dependent variable and dummy variables for the geographic regions as the main independent variables (Fig.  7 ). Additionally, we account for publication years using dummy variables in all our regression analyses to normalize for the time an article was at risk of being cited. To estimate the scientific and clinical relevance of AI-related articles conditional on the underlying quality and content, we run additional models with more than 9000 publication outlet fixed effects. These fixed effects (i.e., dummy variables for each outlet) absorb any confounding effects of time-stationary outlet characteristics on citations, including journals and conference proceedings’ published content and quality. As journals and conference proceedings with all zero outcomes for the dependent variable (i.e., publishing research that is not cited) get automatically dropped from the fixed effects analyses, the sample sizes are smaller in the corresponding regression models. Our results remain consistent when estimating all models on the smaller samples. We present our results as incidence rate ratios (IRRs) relative to the baseline geographic region (Asia). Finally, we track the share of international collaborations over time as the share of research papers featuring at least two authors with affiliations from different countries (Fig.  8 ). We estimate the scientific and clinical relevance of these international collaborations in negative binomial regression models, again controlling for publication year dummy variables, the locale of the lead author, and the total number of authors on the author byline. We characterize the geography and direction of international collaborations by considering the country of the lead author as the outgoing country and counting all dyadic connections between the country of the lead author and any other country of the first 10 authors listed on the focal publication (Fig.  9 ). Importantly, 99% of the publications in our sample list 10 or fewer authors. Analyses are conducted in Stata. Data visualizations are created with Python and Prism. Reporting summary Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article. Data availability The publication data assembled in this study have been deposited in the Figshare database [ https://doi.org/10.6084/m9.figshare.24412099 ]. Source data are provided with this paper. Code availability The computer code to perform the analyses of this study has been deposited in the Figshare database ( https://doi.org/10.6084/m9.figshare.24412099 ). Matheny, M. 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Scientometrics 126 , 3153–3192 (2021). Download references Acknowledgements We thank Jennifer Hahn and Luca Caprari for outstanding research support. LS is supported by the Joachim Herz Foundation. MJL received financial support through the FAIR@UMA program of the University of Mannheim. MJL and TWB received financial support in scope of the Helmholtz Information and Data Science School for Health (HIDSS4Health). The publication of this article was partially funded by the University of Mannheim. Open Access funding enabled and organized by Projekt DEAL. Author information Authors and affiliations. University of Mannheim, Mannheim, Germany Leo Schmallenbach & Marc J. Lerchenmueller Heidelberg Institute of Global Health (HIGH), Medical School, Heidelberg University, Heidelberg, Germany Till W. Bärnighausen Harvard Center for Population and Development Studies, Harvard University, Cambridge, USA Africa Health Research Institute (AHRI), Durban, South Africa Leibniz Center for European Economic Research (ZEW), Mannheim, Germany Marc J. Lerchenmueller You can also search for this author in PubMed   Google Scholar Contributions LS, MJL, and TWB devised the original idea. LS and MJL assembled and analyzed the data. LS and MJL wrote the manuscript. TWB edited the manuscript. Corresponding author Correspondence to Leo Schmallenbach . Ethics declarations Competing interests. MJL is a co-founder and shareholder of AaviGen GmbH, a cardiovascular gene therapy company. The present study is not related to the company. The remaining authors declare no competing interests. Peer review Peer review information. Nature Communications thanks Ravi Parikh, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. A peer review file is available. 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