research paper on expert system

• Open access
• Published: 22 July 2021

A systematic review of the applications of Expert Systems (ES) and machine learning (ML) in clinical urology

• Hesham Salem   ORCID: orcid.org/0000-0002-5296-2311 1 , 2 ,
• Daniele Soria   ORCID: orcid.org/0000-0002-0164-8218 3 ,
• Jonathan N. Lund   ORCID: orcid.org/0000-0001-5195-2181 2 &
• Amir Awwad   ORCID: orcid.org/0000-0003-1288-1493 4 , 5  

BMC Medical Informatics and Decision Making volume  21 , Article number:  223 ( 2021 ) Cite this article

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Testing a hypothesis for ‘factors-outcome effect’ is a common quest, but standard statistical regression analysis tools are rendered ineffective by data contaminated with too many noisy variables. Expert Systems (ES) can provide an alternative methodology in analysing data to identify variables with the highest correlation to the outcome. By applying their effective machine learning (ML) abilities, significant research time and costs can be saved. The study aims to systematically review the applications of ES in urological research and their methodological models for effective multi-variate analysis. Their domains, development and validity will be identified.

The PRISMA methodology was applied to formulate an effective method for data gathering and analysis. This study search included seven most relevant information sources: WEB OF SCIENCE, EMBASE, BIOSIS CITATION INDEX, SCOPUS, PUBMED, Google Scholar and MEDLINE. Eligible articles were included if they applied one of the known ML models for a clear urological research question involving multivariate analysis. Only articles with pertinent research methods in ES models were included. The analysed data included the system model, applications, input/output variables, target user, validation, and outcomes. Both ML models and the variable analysis were comparatively reported for each system.

The search identified n = 1087 articles from all databases and n = 712 were eligible for examination against inclusion criteria. A total of 168 systems were finally included and systematically analysed demonstrating a recent increase in uptake of ES in academic urology in particular artificial neural networks with 31 systems. Most of the systems were applied in urological oncology (prostate cancer = 15, bladder cancer = 13) where diagnostic, prognostic and survival predictor markers were investigated. Due to the heterogeneity of models and their statistical tests, a meta-analysis was not feasible.

ES utility offers an effective ML potential and their applications in research have demonstrated a valid model for multi-variate analysis. The complexity of their development can challenge their uptake in urological clinics whilst the limitation of the statistical tools in this domain has created a gap for further research studies. Integration of computer scientists in academic units has promoted the use of ES in clinical urological research.

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Introduction

In the 1950’s J McCarthy in Stanford University and A Turing in Cambridge University proposed the concept of machine simulation of human learning and intelligence [ 1 , 2 ]. Being keen mathematicians, they advanced the basic mathematical logic into programming languages enabling machines to perform more complex functions. E Shortliffe advanced those systems to develop MYCIN, which successfully simulated the reasoning of a human microbiologist in diagnosing and treating patients with microbial infection [ 3 ]. Their model introduced Expert Systems (ES) to the scientific literature and a ten year review by Liao et al. demonstrated their wide prevalence in the industrial fields with immense applications including health care [ 4 ]. In contrast to Liao’s review, other studies questioned their real time implementation in health care and suggested a lack of their uptake and integration in the health care systems [ 5 ]. This is despite evidence from systematic reviews demonstrating the positive impact of computer aid systems on patients’ outcome and health care [ 6 , 7 ].

This study aimed to systematically review published ES in urological health care with a primary aim to demonstrate their availability, progression, testing and applications. The secondary aim was to evaluate their development life cycle against standards suggested by O’Keefe and Benbasat in their review articles on ES development [ 8 , 9 ]. The later would evaluate the gap between their development and implementation in health care.

The study methodology followed the recommendations outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement (Fig.  1 ). No ethical approval was required because the type of the study waives this requirement.

PRISMA flow chart for the systematic review of articles included in the review of expert systems in urology

Information sources including WEB OF SCIENCE, EMBASE, BIOSIS CITATION INDEX, SCOPUS, PUBMED, Google Scholar and MEDLINE were searched using key words in (Table 1 ). Articles published between 1960 and 2016 were considered and examined against the inclusion criteria. While tailoring the conducted search for each literature database, the key words were combined by “OR” in each domain, then domains were combined by “AND”.

Eligibility criteria

For the primary aim, data search was conducted to yield the collected results then analyse them according to pre-planned eligibility criteria based on the system model, year of production, type and outcome of its validation, functional domain application, variables for input and output, target user and domain. This selection criteria were designed with an objective to identify expert system studies and demonstrate their prevalence, testing, and applications in clinical urology. Only articles and studies written in English were included.

Further qualitative analysis was required to meet the study secondary aim. For this, further data was gathered on credibility (user perception on the system), evaluation (system usability), validation (building the right system) and verification (building the system right) then compare against the standards reported in [ 8 , 9 ].

Data filtering

The resultant reference list of each included article was checked to identify a potentially eligible item that had not been retrieved by the initial search. All retrieved articles were collated in a final reference list on a management software (Endnote, X8), then duplicate studies were removed from the list.

Upon including more than one hundred articles, the rest of the eligible articles were meticulously compared to the ones included, then excluded based on demonstrating clear similarity. This was applied to avoid expanding the size of the data without adding to the study analysis.

ANN was the commonest model to be applied in Urological ES (Fig.  2 ). The rest of the models demonstrated diversity which is consistent with other published industrial systems [ 4 ].

Analysis of Expert Systems (ES) by models (n = 169). ANN was the most common but other systems were applied on different domain as fuzzy neural model (FNM), rule-based system (RBS), fuzzy rule based (FRB), support vector machine (SVT), Bayesian network (BN) and decision trees (DT)

Prostate cancer was the commonest domain for urological ES with most of the system focusing on cancer diagnosis. These systems were applied to various domains (Fig.  3 ), and they were further stratified and analysed according to their core functional application as outlines in the methodology.

Urological domains (n = 168) applied by Expert Systems (ES). Prostate cancer (CaP) was the commonest domain followed by bladder cancer (Bca) then other diseases as benign prostatic disease (BPD), pelvi ureteric junction obstruction (PUJ), urinary tract infection (UTI), renal cell cancer (RCC), vesico ureteric reflux (VU reflux)

Quantitative analysis

Decision support systems.

The main objective of ES in this domain was to facilitate the clinical decision making by identifying key elements from patients clinical and laboratory examinations then refine a theoretical diagnostic or treatment strategy [ 10 ]. They can guide the expert to find the right answer [ 11 ] or take over the decision making to support the none expert as [ 12 ] or even replace both to interact with the patient directly [ 13 ].

They have supported various aspects of urological decision making such as diagnosis, investigations analysis, radiotherapy dose calculation, the delivery of behavioural treatment and therapeutic dialogues.

Urinary dysfunction (U Dys) was the commonest domain to be covered in the decision support system application (n = 9), which could be further categorised into U Dys diagnostic, investigation analysis and therapeutic systems. They have demonstrated a range of methodologies, validation, and target users (Table 2 ) applicable to Decision support systems in Urological domain. For instance, Keles et al. [ 14 ] designed an ES to support junior nurses in diagnosing urinary elimination dysfunction in a selected group of patients while [ 15 , 16 ] systems were able to support any medical user to diagnose urinary incontinence with an accuracy reaching higher than 90%. The target user of most of these systems were predominantly medical health care workers including both experts and none experts, with exception of [ 13 , 17 ] which can be directly used by patients to receive an assessment of their urinary elimination dysfunction followed by a tailored treatment plan.

Prostate diseases were represented in 6 systems while 3 of them modelled by [ 10 , 12 , 20 ] for diagnosing both benign and malignant prostatic disease, namely cancer prostate (CaP).

All systems in this domain were diagnosis support system with exception of [ 19 ] which also provided treatment for benign prostatic hyperplasia (BPH) and [ 11 ] calculated the required radiotherapy dose for treating CaP.

Sexual dysfunctions were modelled in 3 systems where [ 21 ] diagnosed male sexual dysfunction with an accuracy of 89%, while [ 22 ] added a therapeutic model for the same disease with an overall accuracy of 79%. Sexpert by [ 23 ] was the third system in this category developed in 1988 and in fact the oldest ES to be identified from our search in all urological domains. Interestingly this RB system was designed to interact directly with couples suffering from sexual dysfunction where the system responds to their query with a tailored therapeutic dialogue for treating their problem.

Urinary tract infection (UTI) was diagnosed and treated by one of the hybrid fuzzy systems FNM developed by [ 24 ] with an accuracy of 86.8%.

Diagnosis prediction

In this domain, ES quantifying the probability of a clinical diagnosis with a defined margin of error. They simulate a second expert opinion and it has been suggested that their use could eliminate unnecessary invasive investigation as the application of ANN by [ 26 ] could reduce up to 68% of repeated TRUS biopsies to diagnose CaP.

Prostate cancer was the main domain for this application with 19 systems out of 20. Most of them were designed to predict organ confinement before radical surgical excision of the prostate (Tables 3 , 4 ). The target population were patients with clinically localised CaP and their accuracy reached high estimates as in [ 28 ], where the system was able to predict 98% of the low risk group for lymph node involvement using preoperative available date (PSA, clinical stage and Gleason score).

Chiu et al. [ 29 ] modelled a system with clinical variables for patients undergoing nuclear bone scintigraphy for predicting skeletal metastasis. The system was able to predict metastatic disease in the test group with Se 87.5%, Sp 83.3%.

None seminoma testicular cancer was the other domain in this application with the system [ 27 ] able to predict the cancer disease stage (Table 4 ) with accuracy reaching 87%.

Treatment outcome prediction

In this application, ES combined disease and patient related factors to estimate the success of a specific treatment or intervention. As in [ 30 , 38 , 64 , 69 ] where the system predicted the outcome of extra corporeal shock wave (ESWL) for treating kidney stones and [ 74 , 75 ] providing an estimation of cancer recurrence after radical surgical treatment of prostate cancer.

Prostate cancer was also common domain in this application (n = 23). Potter [ 74 , 75 ] described 4 models developed by data acquired from patients with clinically localised CaP and had radical prostatectomy with curative intent. The variables included clinical and histological findings of the surgical specimen and they were able to predict up to 81% who did not have evidence biochemical failure (rising PSA) in their follow up. Hamid et al. [ 76 ] and Gomha [ 77 ] models were not restricted to the clinically localised CaP cohort and their study population included patients at different disease stages and on any treatment pathway. Their models included 2 experimental histological markers (tumour suppressor gene p53 and the proto-oncogene bcl-2) in their input variables and the estimated predictive accuracy of the patient response to treatment were reaching 68% and 80% ( p  < 0.00001) respectively.

Nephrolithiasis treatment was expressed by 6 other systems applying the treatment outcome prediction concept. Cummings et al. targeted this group in his ANN [ 78 ] where he trained his network with patients’ data treated at the emergency service of 3 centres with ureteric stones, to identify patients failing conservative management and requiring further intervention. When tested on a different set of 55 cases, the system correctly predicted 100% of the patients who passed the stone spontaneously with an overall accuracy of 76%.

Extra corporeal shockwave lithotripsy (ESWL) is one of the favourable interventions in the nephrolithiasis treatment domain. The stone here receives strong external shock waves, which can subsequently reduce it into small fragment and eliminate the need for direct instrumentation of the renal tract. Their reported success rate can only provide a generalised prediction of outcome to the individual case and ANN was capable of providing an alternative multivariate analytical tool in the 4 models developed by [ 30 , 38 , 64 , 69 ]. They estimated high accuracy of their models (Table 5 ), as in [ 64 ], the system predicted 97% of the patients who were confirmed to be stone free following ESWL for treating ureteric stone.

Paediatric pelvi-ureteric junction obstruction is primarily treated conservatively unless there is any evidence of renal function compromise, recurring infection or worsening radiological findings. For the failing group, pyeloplasty is the second line of treatment and [ 81 ] developed an ANN to estimate the success rate of this procedure for each individual case by predicting the post-operative degree of hydronephrosis with a reported 100% accuracy in the small tested sample.

Vesico ureteric reflux or reflux uropathy is another paediatric disease, characterised by back flow of urine from the bladder into the ureter through incompetent Vesico ureteric functional valve. Treatment is primarily conservative as it can be a self-limiting disease or surgery to reimplantation the ureters or endoscopic injection of bulking agent at the ureteric orifices [ 80 ]. The study authors trained a neural network using 261 cases whom have received endoscopic injection and the system predicted 94% of the patients who did not benefit from the treatment [ 80 ].

Laparoscopic partial and radical nephrectomy were the domain of the [ 82 ], which was developed by multi institutional case data (age, co-morbidities, tumour size, and extension) of patients having laparoscopic partial or radical nephrectomy. The system was able to predict the length of their postoperative hospital stay with an accuracy of 72%.

Bladder cancer can be treated with complete bladder excision and [ 79 ] developed systems to predict the cure rate with an accuracy of 83%.

Recurrence and survival prediction

The ES in this domain aimed to provide individualised risk analysis tools estimating the disease specific mortality and recognising the group whom may benefit from more aggressive or adjuvant treatment.

Bladder cancer survival and recurrence prediction following radical cystectomy (RC) with curative intention was the commonest domain in this application (24 out of 26 total systems). The lymph nodal involvement is highly predictive of the recurrence and these patients are considered for adjuvant or neoadjuvant systemic chemotherapy. The node free cohort will include high-risk patients who were not identified by the conventional linear stratification system. Catto et al. developed a FNM system to identify this high risk group in the nodal free cohort by predicting the disease recurrence rate (Se 81%, Sp 85%) and their survival with a median error of 8.15 months [ 92 ]. The high-risk group identified by this model can benefit from systemic treatment post cystectomy to improve their disease related morbidity and mortality [ 95 , 96 ]. The 5 years survival post cystectomy was the output of 2 other ANN with a high prediction efficacy of 77% and 90% respectively (Table 6 ) [ 97 , 99 ].

Renal cell cancer is primarily treated with partial or radical nephrectomy for clinically localised disease with systemic therapy for the metastatic disease. There is still a degree of uncertainty in stratifying individual disease risk in order to predict the indication and outcome of systemic therapy in the group with distant metastasis. Vukicevic et al. [ 98 ] attempted to clarify this uncertainty by training a neural network with patients’ data who had nephrectomy (partial or radical) and received systemic therapy. The mature model predicted the patients who survived the disease at 3 years with an overall accuracy of 95% (CI 0.878–0.987).

None seminoma testicular cancer 5 years recurrence was the domain of [ 118 ] ANN. The system was trained with multicentre data and in its testing phase and predicted 100% of the patients who did not suffer from disease recurrence at 5 years with an overall predictive accuracy of 94% (AUC = 87%).

Predicting research variables

In academia, testing a hypothesis for ‘factors-outcome effect’ is a popular quest and the standard statistical regression analysis tools may not be effective for data contaminated by irrelevant variables [ 119 ]. AI can provide an alternative methodology in the analysis to identify variables with high correlation to the outcome by applying machine learning as in ANN. The area under the curve (AUC) is estimated for the system predictive accuracy applying all researched variables. Those research variables can be given random values or randomised then the AUC is re estimated for comparison with the original [ 120 ]. Only variables that decreases the AUC are considered significant and the wider the discrepancy of the AUC the more significant they are (Table 7 ).

Prostate cancer was a common domain in this application with a total of 15 systems analysing predictive factors for diagnosis of cancer, response to treatment and quality of life with prostatic disease. One of the hot topics in Urological cancer is discovering alternative CaP diagnostic markers since serum PSA is not sensitive for distinguishing benign from malignant disease. Stephan et al. investigated the diagnostic value of three markers in this domain: Macrophage inhibitory cytokine-1, macrophage inhibitory factor and human kallikrein 11 [ 108 ]. These were used as variables (nodes) in ANN models and compared their accuracy to the linear regression of %fPSA. They have reported that only the ANN model including all three variables was more accurate (AUC 91%, Se 90%, Sp 80%) than all other models proving his hypothesis that they are only relevant as when combined.

Similarly, another study estimated the predictive values of serum PSA precursors (-5, -7 proPSA) in diagnosing prostate cancer using and comparing the accuracy to %fPSA [ 107 ]. The -5, -7 pro PSA were only significant in the cohort with PSA between 4 and10 µg/l and did not improve the predictive accuracy when added to the %fPSA. The same author tested this hypothesis on another free PSA precursor (-2 proPSA) by developing ANN with the %p2PSA (-2 ProPSA: fPSA) among other disease variables, which have improved the system accuracy (AUC 85% from 75%) [ 120 ].

Three systems evaluated the presence of bcl-2 and p53 (tumor suppressor genes) as a predictive variable for response to prostate cancer treatment [ 76 , 77 ]. Their combination was reported to be significant (Ac 85%, p  < 0.00001) in [ 77 ] but [ 76 ] found that only bcl-2 is relevant in the other two models (accuracy 63–68%).

Bladder cancer diagnosis and disease progression was the second most common domain with 13 systems. Kolasa et al. [ 110 ] have modeled an ANN with three novel urine markers: urine levels of nuclear matrix protein-22, monocyte chemoattractant protein-1 and urinary intercellular adhesion molecule-1, to predict the diagnosis of bladder cancer and it succeeded in predicting all cancer free patients when the three variables were used as a group. Catto.et al. [ 119 ] developed two AI models (ANN & FNM) performing microarray analysis on genes associated with bladder cancer progression. Their models narrowed down these genes from 200 to 11 progression-associated genes out of 200 ([OR] 0.70; 95% [CI] 0.56–0.87), which were found to be more accurate than the regression analysis when compared to the specimen immunohistology results.

Kolasa et al. [ 110 ] model predicting the pre-histology diagnosis of malignancy based on urine level of novel tumour markers. Their ANN was found to be more accurate (Se 100%, Sp 75.7%) than haematuria diagnosed on urine dipstick (Se 92.6%, Sp 51.8%) and atypical urine cytology (Se 66.7%, Sp 81%).

ESWL of renal stones was the research domain of [ 30 , 69 ], where they aimed at identifying significant variables correlated to the treatment outcome (stone free) and developing a predictive model. Chiu et al. [ 69 ] model did not recognise residual fragments following ESWL as a significant risk for triggering further stone growth and [ 30 ] identified these factor: positive BMI, infundibular width (IW) 5 mm, infundibular ureteropelvic angle 45% or more (IUPA), to be all predictive of lower pole stone breaking and clearance.

Benign prostatic hyperplasia was modelled in a system [ 114 ] to link the disease specific clinical and radiological factors with the disease progression in patients with mild disease (IPSS < 7) and not receiving any treatment. His ANN identified: obstructive symptoms (Oss), PSA of more than 1.5 ng/ml and transitional zone volume of more than 25 cm 3 , to be correlated to disease progression and can accurately predict 78% of the cohort who will need further treatment.

Urinary dysfunction diagnosis accuracy by clinical symptoms was compared to urodynamic findings in female patients with pelvic organ prolapse by [ 115 ] and both the linear regression and ANN models could not establish relation between the symptoms and urodynamic based diagnosis hence dismissing the hypothesis of only relying on clinical symptoms to reach an accurate diagnosis and replace the need for urodynamics study.

Hypogonadism (Hgon) was represented in [ 133 ] system where the diagnosis was made based on patient’s age, erectile dysfunction and depression with AUC of 70% (p < 0.01).

Image analysis

This one of the advancing applications of AI in medicine where the system either analyse the variables in the reported medical images as data input or identifies these variables through a separate image analyser without the need for expert to report the scan or images. The first category was included among other systems mentioned above as in the diagnosis prediction domain where [ 47 ] included different variables from TRUS in the system input to predict CaP diagnosis. In this domain, we focused on the other group where the images are presented to the machine in the form raw data translated by the image analyser and the system will then apply their machine learning to identify the cause effect pattern (Table 8 ).

Prostate cancer image analysis was modelled in 10 systems to enhance diagnostic accuracy as in [ 126 ] and disease progression prediction as in [ 128 ]. The first system represented each TRUS image pixel as one variable or neuron in a pulse coupled neural network and trained their system with 212 prostate cancer images to segment prostate gland boundary with an average overlap accuracy (overlap measure = difference between PCNN boundary and the expert) of 81% for ten images [ 126 ].

The other 4 systems analysed histological images of a cohort of patients post RP with clinically localised CaP to predict the disease progression. The histological images were given coloured coding and analysed by the system that used variables as % of epithelial cell and glandular Lumina to identify the high risk group for disease recurrence with an accuracy reaching 90% [ 128 ].

LUT disease urine cytology images were analysed by 2 models in [ 123 ], which identified all patients with benign disease with an overall accuracy of 97%.

Nephrolithiasis stone biochemistry analysis can be achieved through an expert analysis of infrared spectroscopy which was simulated by [ 124 ] where the infrared spectra wavelength numbers were modelled as input variables and the system prediction accuracy of the expert analysed stone specimen had a root square mean error of 3.471.

Qualitative analysis

The same articles were considered for the qualitative analysis against the four stages (validation, verification, evaluations and credibility) reported in Okeefe industrial survey [ 8 ] and Benbasat article [ 9 ]. The completion of the four stages examined in this qualitative analysis was demonstrated by none of the included systems. There is a possibility that some of these missing stages has been performed but not published in the scientific literature.

Validation was performed by almost all the systems (166 out 169) with varying degree of study strength, bias, and limitations (Table 9 ). Most of the data driven systems (ANN, SVM, BN, kNN and FNM) were validated by the ROC and AUC by having a training and validation set or cross validation or applying the leave one out technique. Samli et al. enhanced the validity of their system by estimating the kappa statistics with the ROC [ 134 ].

Evaluation was only performed by a small fraction of these systems (n = 6). Their evaluation was aiming at the user or the expert but rarely both. There is no evidence to support that these were performed at early stages to determine the substantiality of the system to the user.

System credibility and verification were never performed. It would be implied that the verification was performed to an extent but not reported as it is a technical part of the development.

‘System development limitation and bias evaluation’ demonstrated an overall acceptable validation methodology with valid statistical analysis. However, a few observed limitations (Table 9 ) were reported with the common encounter being the consideration human opinion as a gold standard (n = 9). For instance, the gold standard in diagnosing prostate cancer is tissue biopsy confirmation. The interpretation of the expert clinical diagnosis as the gold standard reference can lead to statistical errors and invalidate the study.

Expert Systems are widely available in Urological domains, with a large range of models, applications, domains, and target users including patients, students, non-experts, experts, and researchers. The number of published systems has risen over the years but with a consistent lack of publications reporting their real time testing or healthcare implementation (Fig.  4 ).

Expert System (ES) analysis by year of publication showing an upward trend and increase in number of publications. Systems were included according to the keywords for expert system models and applied in urological domains

There is an increasing interest in analysing this gap which is reflected from the scope of AI historic review articles which aimed to only familiarise the readers with ES existence and application [ 33 , 125 ]. In fact, the majority had a relatively narrow scope on the evolution and application of one ES models (artificial neural network) in prostate cancer diagnosis. Recently, similar to our research, there has been more interest in AI validation, and lack of uptake despite the faith in their ability. Therefore, in this study we quantified ES progression and applications in Urology while examining their developmental life cycle.

It was evident that CaP was the commonest domain in almost all applications contributing with more than two thirds of the systems (91 systems in total). Different aspects of this domain have been simulated by these systems to include diagnosis, therapeutics, predictions of disease progression or treatment outcome, researching variables and medical images analysis. Most of these systems were simulating urologist cognitive function with little guidance on their benefits and how they can be implemented to improve cancer decision making.

In industry, this is usually performed before the system development by evaluating the system usability from the user perspective. This part has lacked or not been acknowledged in the published studies and is possibly a core reason for the lack of their integration in urological health care. Furthermore, none of these systems has been a subject to live testing in a well-designed study to prove its efficacy over standard tools or in the clinical context to prove its validity to justify their complex structure to AI novice health care professionals. The qualitative analysis demonstrated that validation is the only stage of the development cycle to be applied by most of the systems and there is a lack of system evaluation, credibility, and verification. The evaluation can be subdivided into usability (usually by average user), utility and system quality (by experts) [ 9 ]. Despite the crucial stage of ES development, there has been a lack of attention in the published articles to integrate it into the development life cycle. This can mean the whole system can fail and also challenge its uptake [ 8 ].

An example can be drawn from this review where the majority of the systems focused on CaP diagnosis and treatment. Their implementation would be challenged by the standard decision-making tools of the cancer multidisciplinary team and the ethical concerns of relying on ANN in making such life changing and expensive decision. The utility analysis of those ES would have been essential for tailoring their development for real time applications where they can be more substantial to the user. One example is lack of community-based systems for the initial referral of suspected cancer patients and follow up of stable disease, where NICE have identified a need for such decision support models [ 152 , 153 ].

There was a wide diversity of modelling in Urological ES with ANN being the most common model in this review. These would bypass the need for direct learning from experts and the exhaustive process of knowledge acquisition, which is a core requirement for knowledge-based systems to attest the whole system progress [ 55 ]. However, their analytical hidden layer of nodes “black box phenomenon” has been a subject for wide criticism and rejection from clinicians due to lack of transparency and understanding of its function.

Stephan et al. suggested a statistical solution to identify the variables significance by performing sensitivity analysis [ 154 ]. This estimates the variation of the AUC with introduction or elimination of each variable. This can only reflect the significance of each variable but does not explain how the cases are being solved nor quantify this to the user in a standard statistical value. This can be useful in research as they can identify significant variables in a large set data and has been successfully applied in the field of academic urology as in [ 119 ] where the system successfully identified the relevant gene signature for bladder cancer progression which saved time and cost of microarray analysis of all suspected genes.

Holzinger et al. emphasised on the importance of the explicability of the AI model specially in medicine which is a clear challenge for machine learning due to their complex reasoning [ 155 ]. Their study attempted to simplify the explanation by classifying the systems into post-hoc or ante-hoc. In post-hoc, explanations were provided for a specific decision as in model agnostic framework where the black box reasoning can be explained through transparent approximations of the mathematical models and variable [ 156 , 157 ]. Those are reproduced on demand for a specific problem rather than the whole system which can shed more light on the system function. It is not certain if those can be easily interpreted by the AI novice clinician, but it has provided more explicit models for tackling the black box phenomenon.

Knowledge based systems can be explained by ante hoc models where the whole system reasoning can be represented. Those systems rely on expert knowledge in their development and face the bottle neck phenomenon in their applications. Furthermore, they are not always successful in identifying and mapping multilinear mathematical rules and machine learning is mandatory or at least more efficient [ 155 ]. Bologna and Hayashi et al. suggested that machine learning is more successful in complex problem solving with inverse relation between the machine performance, and it is built-in transparency [ 158 ].

Another common aspect lacking in these articles was the coupling of their system development methodology with the medical device registration requirements. This is essential as ES often function as standalone software with no human supervision to their calculation. This categorises the system as a medical device with mandatory perquisite to register with the relevant authorities as Medicines & Healthcare products Regulatory Agency in the UK [ 5 ].

Cabitza et al. compared AI validation to other medical interventions as drugs and emphasised on considering the “software as a medical device” [ 159 ]. Unlike other devices or drugs, AI models in healthcare are unique in being more dynamic which should be reflected in their validation cycle. They also quoted the known term “techno-vigilance” to learn from other medical device validation pathways. They recommended different outlook to validation where it is broken down to statistical (efficacy), relational (usability), pragmatic (effectiveness) and ecological (cost-effectiveness) with available standards for those steps (ISO 5725, ISO 9241 and ISO 14155). The latter is viewed as a novel standard for evaluating the cost benefits of applying specific AI model in healthcare which would require longitudinal modelling of health economics [ 159 ]. This was evidently lacking in articles that were included in our review and in fact most of the studies were non-randomised and retrospective.

Similarly, Nagendran et al. systematically analysed studies that compare AI performance to experts in classifying medical imaging into diseased and non-diseased, they concluded that AI performance was non-inferior to human experts with potential for out-performing [ 160 ]. Their 10 years review identified from literature 2 randomised clinical trials and 9 prospective non-randomised trials extracted from a total of 10 and 81 studies, respectively. Their review assessed the risk of bias using PROBAST (prediction model risk of bias assessment tool) criteria for non-randomised studies. The tool is designed for identifying the risk of bias by analysing four domains (participant, predictors, outcome, and analysis) [ 161 ], which is applicable to systematic review analysing prediction model with a target outcome.

In our study, as there was no unified outcome for the included prediction tools, the scope was on the role of validation rather than the outcome. Therefore, those tools assessing the risk of bias were not utilised due to the wide gaps in the tool checklist between the included articles. Such study design and data heterogeneities were also evident in Nagendran et al. and similar to our study, data synthesis was not possible. This will pose a challenge reinforcing the application of AI models in healthcare due to lack of level 1 evidence which is mandatory in healthcare for accepting a novel intervention.

Finally, the quality of the data analysis was beyond the scope of our systematic review despite being essential for developing quality AI systems. Cabitza et al. examined this gap and focused on the data governance [ 161 ]. There has been very limited evidence on data quality appraisal and standards with call for further research and allocation of more resources specially in healthcare where the data are notoriously limited with errors or discordance.

The potential application of AI in urology with focus on its future application has been recently discussed by Eminaga et al. [ 162 ]. They have shown an increasing interest in urology research, but with a challenged mechanistic update due to the model complexity and lack of end user understanding of its design and function. Furthermore, they identified discrepancy between AI engineering and clinical application which reflects some lack of communication between both disciplines.

This can be either a consequence or a cause for lack of clinical utility testing, which increases the need for research in this domain to be incorporated in the software development [ 163 ]. In fact, it has been recommended to perform the utility test before developing the system to tailor its application [ 164 , 165 ]. Despite having different methodology to our systematic review, the recommendations were similar with strong emphasis on the lack of utility testing and its impact on AI uptake in healthcare [ 166 , 167 , 168 ].

ES have been advancing in Urology with demonstrated versatility and efficacy. They have suffered from lack of formality in their development, testing and methodology for registration, which has limited their uptake. Future research is recommended in identifying criteria for successful functional domain applications, knowledge engineering and integrating the system development with the registration requirement for their future implementation in the health care systems.

Availability of data and material

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Abbreviations

Artificial intelligence

Artificial neural networks

Acute prostatitis

Bladder cancer

Backward chaining

Biochemical failure

Bacille Calmette–Guérin

Back propagation neural network

Benign prostatic disease

Benign prostatic hyperplasia

Computer aided diagnosis

Case based reasoning

Chronic prostatitis

Cross validation

Digital rectal exam

Erectile dysfunction

Expert Systems

Forward chaining

Family history

Fuzzy logic systems

Fuzzy ontology

Fuzzy neural modelling

Fuzzy rule-based systems

Follicular stimulating hormone level

Genetic algorithm

Gleason score

Hypogonadism

Human kallikrein 11

Incontinence

Information systems

Irritative symptoms

Information technology

Infundibular ureteropelvic angle

Infundibular width

Knowledge acquisition

Kaplan Meir Survival Plot

Knowledge engineer

Laparoscopy

Luteinising hormone level

Leave one out

Lower urinary tract

Learning vector quanitizer

Macrophage inhibitory cytokine-1

Macrophage inhibitory factor

Medical history

Machine learning

Medicines and Healthcare products Regulatory Agency

Nephrectomy

Nephrolithiasis

National Institute for Health and Care Excellence

Negative predictive value

None seminoma testicular cancer

Obstructive symptoms

Pelvic organ prolapse

Prostate cancer

Positive predictive value

Prolactin level

PSA density

PSA velocity

Post void residual

Maximum flow rate

Requirement analysis

Rule based reasoning

Radical cystectomy

Renal cell carcinoma

Receiver operating characteristic

Radical prostatectomy

Single centre

Sensitivity

Stable prostate cancer

Specificity

Total prostatic volume

Trans rectal ultrasound scan

Total Testosterone

Transitional zone PSA density

Transitional zone volume

Urodynamic study

Urinary dysfunction

Urinary tract infection

Verification and validation

Vesico-ureteric reflux

Percentage free/total PSA

Percentage p2PSA/fPSA

Urinary incontinence

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Hesham Salem

University Hospitals of Derby and Burton NHS Foundation Trust, Royal Derby Hospital, University of Nottingham, Derby, DE22 3DT, UK

Hesham Salem & Jonathan N. Lund

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Daniele Soria

NIHR Nottingham Biomedical Research Centre, Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, NG72UH, UK

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All listed authors have read and approved the final manuscript. All listed authors contributed sufficiently to take responsibility for the whole content of the manuscript following the criteria in ICJME guidelines of authorship rights and responsibilities. HS for conceptualisation, literature review, data curation, formal analysis, methodology and original writing, review, and editing. DS and JNL for supervision, writing review and editing. AA for field investigation, validation, draft review and editing. All authors read and approved the final manuscript.

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Salem, H., Soria, D., Lund, J.N. et al. A systematic review of the applications of Expert Systems (ES) and machine learning (ML) in clinical urology. BMC Med Inform Decis Mak 21 , 223 (2021). https://doi.org/10.1186/s12911-021-01585-9

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• Reddy B Fields R Ogden C Gamess E (2022) From past to present Proceedings of the 2022 ACM Southeast Conference 10.1145/3476883.3520211 (167-172) Online publication date: 18-Apr-2022 https://dl.acm.org/doi/10.1145/3476883.3520211
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An Overview of Expert Systems

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• A. I. Kokkinaki &
• K. P. Valavanis  

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Artificial Intelligence and its subfield Expert Systems have reached a level of maturity, particularly in recent years, and have evolved to the point that a Knowledge-Based Expert System may reach a level of performance comparable to that of a human expert in specialized problem domains like, Computer Systems, Computing, Education, Engineering, Knowledge Engineering, Geology, Medicine and Science. An Expert System is a high performance problem solving (software) computer program, capable of simulating human expertise in a narrow domain.

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Tzafestas, S.G., Kokkinaki, A.I., Valavanis, K.P. (1993). An Overview of Expert Systems. In: Tzafestas, S. (eds) Expert Systems in Engineering Applications. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-84048-7_1

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A Case Study on Expert Systems

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Intelligent libraries: a review on expert systems, artificial intelligence, and robot

Library Hi Tech

ISSN : 0737-8831

Article publication date: 30 June 2020

Issue publication date: 21 June 2021

This paper reviews literature on the application of intelligent systems in the libraries with a special issue on the ES/AI and Robot. Also, it introduces the potential of libraries to use intelligent systems, especially ES/AI and robots.

Design/methodology/approach

Descriptive and content review methods are applied, and the researchers critically reviewed the articles related to library ESs and robots from Web of Science as a general database and Emerald as a specific database in library and information science from 2007–2017. Four scopes considered to classify the articles as technology, service, user and resource. It is found that published researches on the intelligent systems have contributed to many librarian purposes like library technical services like the organization of information resources, storage and retrieval of information resources, library public services as reference services, information desk and other purposes.

A review of the previous studies shows that ESs are a useable intelligent system in library and information science that mimic librarian expert’s behaviors to support decision making and management. Also, it is shown that the current information systems have a high potential to be improved by integration with AI technologies. In this researches, librarian robots mostly designed for detection and replacing books on the shelf. Improving the technology of gripping, localizing and human-robot interaction are the main concern in recent librarian robot research. Our conclusion is that we need to develop research in the area of smart resources.

Originality/value

This study has a new approach to the literature review in this area. We compared the published papers in the field of ES/AI and robot and library from two databases, general and specific.

• Library system
• Intelligent systems
• Artificial Intelligent (AI)
• Intelligent library
• Smart library
• Expert System (ES)

Asemi, A. , Ko, A. and Nowkarizi, M. (2021), "Intelligent libraries: a review on expert systems, artificial intelligence, and robot", Library Hi Tech , Vol. 39 No. 2, pp. 412-434. https://doi.org/10.1108/LHT-02-2020-0038

Emerald Publishing Limited

Copyright © 2020, Asefeh Asemi, Andrea Ko and Mohsen Nowkarizi

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial & non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at: http://creativecommons.org/licences/by/4.0/legalcode .

1. Introduction

Understanding the nature of the information needs and defining this need for the system,

Identifying information resources that are relevant to information needs,

Evaluation of existing information resources, evaluation of retrieved information,

Organizing existing information resources, organizing selected information from items retrieved,

Managing existing information resources, managing retrieved information,

Using existing information resources, using retrieved information,

Information and knowledge analysis,

Converting information to knowledge,

Dissemination and transfer of information and knowledge,

Interaction and exchange of information and knowledge.

In WoS, which articles did authors write about ES/AI and robots’ application in the library?

In Emerald Insight, which articles did authors write about ES/AI and robots’ application in the library?

2. Artificial intelligence

AI applies to different sciences. We can say in the library and information science, it more uses in scientific databases and library systems. Such as behavioral science, social sciences, psychology, management and library science and information science. It is related to some of the systems that apply different forms of intelligence such as learner systems, inferior systems, systems with natural language understanding or natural language interpretation, systems with visual scene perception and systems that perform other types of feat that require human types of intelligence ( Bavakutty and Salih, 2006 ). In this branch of the science that involves machines, solutions are utilized to solve complex problems of human behavior. We can present computer-based algorithms based on human behavior and knowledge in using systems. “It is an interdisciplinary field making use of concepts from various fields like cybernetics, information theory, psychology, linguistics, logic, etc. it can use to simulate human behavior and for computer ailed instruction, ES, robots and for NLP. It can also use for Intelligent Retrieval from databases” ( Bavakutty and Salih, 2006 ). In this way, computer software and the use of various computer-based products help in the operation of various types of libraries and their public services and the generation of output products. Automation implies the degree of mechanization where the routines and receptive jobs or operations are left to be performed by machines with little or no intervention by human beings. Lesser the degree of human intervention, greater the degree of automation; this does not mean that automation does away with human beings. On the contrary, human beings are relieved of routine chores, giving them more time for tasks, which require their intelligence. In view of the various features of a modern computer system, we find that it has been applied in several areas of library work. Book acquisitions, cataloging, serials control, and circulation, information retrieval and dissemination, interlibrary loan, cooperative acquisition and cataloging have been automated in the library ( Lakshmikant and Vishnu, 2008 ).

3. Intelligent systems

Intelligent systems (ISs) are defined as any formal or informal system that is able to obtain and process data, to interpret the data by applying technologies of artificial intelligence and business intelligence and to provide reasoned judgments based on that to decision makers as a basis for action ( Sharda et al. , 2017 ).

ISs are computer-based systems that help in the task of subject indexing can be thought of as an ES ( Lancaster, 1997 ). Lancaster has a clear statement relating to the scope of AI: “Computer programs have been developed, which exhibit human-like reasoning, which may be able to learn from their mistakes and which quickly and cleverly perform tasks normally done by scarce and expensive human experts.” AI has a wide application area. Figure 1 gives a good idea of this coverage.

Technologies that are frequently used in intelligent systems: machine learning, case-based reasoning, genetic algorithms, fuzzy logic and natural language processing (NLP).

NLP is another facility of an intelligent system that can use to retrieve information needs from different scientific databases. In the information retrieval process, the user can state his information requirement in natural language, making the searching more easily and fruitful this allows users to state complex retrieval languages ( Bavakutty and Salih, 2006 ).

Business intelligence (BI) as is the set of techniques and tools for the transformation of raw data into meaningful and useful information for business analysis/decision support purposes ( Sharda et al , 2017 ). BI solutions include data access, storage, data analysis and visualization technologies to support better decision making.

4. Expert systems

Expert systems (ESs) are computer-based systems that simulate human decision making. They can integrate with information systems to improve their accuracy and performance ( Singh et al. , 1996 ). Various librarian ES has been developed. Waters (1986) designed the National Agricultural Library’s microcomputer-based ES to help users obtain answers to simple reference questions. In general, they ask questions from the user and take the user’s answer as input, then explain the rationale for decision results. In general, these systems consist of two main elements: A knowledge base and inference engine. The knowledge base encompasses all the information needs that human/librarian experts are using to decide. This information is present in the knowledge base as facts and rules. ESs can make much better decisions than librarian decision makers because their knowledge base can involve the experiences of a team of the best experts. The manner of librarian experts to make decisions is emulated for the design rules of the knowledge base. The rules are consisting of two main phases: “if phase” and “then phase.” The “if phase” is consisting of conditions, and the “then phase” is consisting of results. ESs are distinguished from other computer systems with the application of reasoning through the inference engine. The inference engine simulates human decision makings based on the knowledge base and a rule base ( Figure 2 ).

Knowledge-based indexing ( Amin and Razmi, 2009 );

Natural Language Processing and abstracting ( Albayrak and Erensal, 2004 );

Reference work ( Amin and Razmi, 2009 );

Cataloging ( Weiss, 1994 ) and ( Amin and Razmi, 2009 );

Online information retrieval ( Bellman and Zadeh, 1970 ), ( Sacchanand and Jaroenpuntaruk, 2006 ) and ( Bavakutty and Salih, 2006 );

Using intelligent interfaces in online information storage and retrieval systems;

Information needs analysis and representation, including different services, such as classification, indexing and abstracting;

Reference services;

Development of collection;

Hypertext and hypermedia ( Bavakutty and Salih, 2006 ).

5. Methodology

Descriptive and content review methods are applied to the study. The researchers critically reviewed the articles related to ES/AI and robots in the library. According to this review, the application of ES/AI and robots classified as the following:

Technology : The articles surveyed and evaluated the information management systems in the libraries belongs to this group. These articles relate to usability and implementation. They do not propose or propose an information system or model.

Resource : These articles related to information resources. This category may include the selection, acquisition and use of information resources.

User / End-user : Existing information and knowledge systems/models are usually working based on the opinion of experts/users and end-user behavior. Therefore, applying ES technologies such as inference engine and fact/rule base will improve the performance and accuracy of considered systems.

Service : The articles in this group have proposed an ES or related technology and methods that can be connected and included in ESs to present public or technical services. The public services present to end-users to fulfill their information needs and technical services present to the librarians or any professional user in library activities.

6. Findings

The purpose of the study is to review the articles on intelligent libraries and the use of ES/AI and robots in the libraries. Based on the research questions, the findings presented in two sections. The first section is related to the review of the articles in WoS as a general database in different subjects. The second section is related to the review of the articles in Emerald as a professional database in the Library and Information Science. According to this review, the application of ES/AI and robots classified into four classes such as technology, resource, user/end-user and service.

6.1 ES/ AI and robots’ application in the library (WoS)

The topics of “expert system” and “library” were searched in the WoS database on 10th Oct 2017. We found 1,208 documents related to this topic. Then we have refined the topics through “Research Area,” “Document Type.” In the research area, we selected the area of “Information Science, Library Science.” We chose “article” for “document type” and excluded unrelated articles. Finally, found 14 articles as a result, which are shown in Table 1 .

The review of papers shows the fading of the ES/AI in recent studies. It is found that the majority (46%) of the paper worked on the experts’/users’ behavior. This is even though no research has been done on the use of intelligent resources using ESs between the years 2007–2017 on the WoS ( Figure 3 ). However, the studies that are related to information systems have a closed relation with the knowledge and opinion of experts. Using ES technologies such as inference engine and fuzzy rule base may increase the accuracy of them. Therefore, the current information systems can be improved by integration with ES technologies. ESs use in intelligent libraries. In general, the information provided to users in a library leads to a change in the behavior of the user’s knowledge and creates learning. The intelligent library uses an appropriate protocol for the exchange of information. This protocol is unique, and it is designed to help, confirm or perform the terms of the agreement. The terms of the agreement include a series of guidelines that will be carried out automatically. These guidelines relate to information sources, services, and technology for distributing and exchanging information. For operating a smart library, resources and services must be available under the agreement. All users must use the digital signature and agree to the terms of the agreement. Smart libraries can exchange information based on the internet of Things (IoT).

Recently the researchers tried to increase the ability of librarian robots by applying the new methods. We searched for the topic of “Librarian robot” using WoS on 10th Oct 2017. Then we limited the results to the duration of 2007–2017. We excluded unrelated articles and finally found 15 articles and proceeding papers as a result, which is shown in Table 2 . In this table, we determine the research area related to applied methodologies of papers in the “Research area of publication source.” A summary of the applied method is explained in “Method,” and the main contribution of papers is mentioned in “contribution.”

The most recent papers that are related to librarian robots are in the area of service ( Figure 4 ). Improving the technology of gripping, localizing and human-robot interaction are the most discussed issues in librarian robots. Librarian robots can be used in large libraries. This robot reduces a lot of common and duplicate activities in different places of the library, especially at the library’s repository. For example, this robot can be helpful in shelf-reading activity. There are some imaginations that the use of librarian robot creates a gap between information and people. Smart libraries and librarian robots are always faced with this challenge. But not a way out of using new technologies, because the development of information does not coincide with the development of expert human resources. In many libraries, librarian robots can be helpful in solving library problems. Only the small number of the studies are related to resources. It is shown that we need to develop our research in this area.

The library should take special care of every aspect related to the man-machine interface: favoring systems standardization, avoiding the accumulation of different equipment, using a clear, brief and direct language, including images and sound, representing reality and reflecting the human mental patterns ( De Prado, 2000 ). AI techniques such as genetic algorithms, artificial neural networks, ESs, and fuzzy logic or hybrid methods can improve librarian robots to reflect human mental patterns.

6.2 ES and robot’s application in the library using Emerald Insight [1]

Table 3 shows the review of the papers in the field of ES/AI and robots, and library exported from the Emerald Insight as a specific database in the library and information science.

Figure 5 shows the most recent papers exported from Emerald Insight, which are related to ES/AI and robot in the library are in the area of service. The finding is the same as the exported papers from the WoS database.

The following trends show a line graph of the relative frequencies across the main category in the abstracts of the articles ( Figure 6 ). The thematic interaction was observed in the main categories of the articles based on their keywords. Most common categories in the abstracts are digital, information, library, search, and user.

Figure 7 shows a line graph of the relative frequencies across the main category of the keywords of the articles. The thematic interaction was observed in the main categories of the articles based on their keywords. Most common categories in the abstracts are digital, information, Internet, library, and systems.

7. Discussion

The ES should be considered only when development is: “possible,” “appropriate,” and “justified” ( Lancaster, 1997 ). This question must be answered before we initialed an ES project. Waters (1986) gives some good guidelines on when we should consider using ESs. An ES has received a lot of attention from the research community in the 1980s. Unfortunately, much of the writing sensationalized the field expectations dramatically ( Lakshmikant and Vishnu, 2008 ) fueled by public expectations began to over-promise misconceptions about what AI can and cannot do arise and they persist today. Many rushed into the field in search of quick answers and quick profits. Several Al researchers saw what was happing and feared a backlash. Once all the excitement wore off during 1988–90, things did begin to change some of the realities and limitations of the AI techniques became evident. An AI backlash has resulted in ascertaining to an extent, but fortunately, it has not been wide-scaled instead. The optimism remains with a better sense of realism than before, and both the benefits and limitations are better appreciated.

An expert of the problem available;

Experts have the time for the ES development project;

Experts can articulate their knowledge and methods;

The problem is not too complex, but knowledge intensive;

The problem is not poorly understood;

The problem requires cognitive skills only.

Reliable visual localization;

Robust and fault-tolerant force-guided extraction;

Performance adequate for books of different sizes and thicknesses;

Active book searching;

Combine navigation and active vision;

Fault-tolerant probabilistic strategy.

In the context of robot librarians and AI has been investigated by limited number of studies. In the database of Emerald, only one article was found ( Yao et al. , 2015 ). They introduced a collaborative library service based on artificial intelligence. They developed an intelligent robot called Xiaotu (female). The task of this robot is to provide online reference services. Four factors are important in the success of this robot: artificial intelligence, self-learning, vivid logo and language, and modular architecture ( Yao et al. , 2011 ). Yuehu and Yanqing (2012) studied using the internet technology of objects. They have tried to look at smart sets along with the robot librarian. Then compare the smart library with other libraries. Kyrarini et al. (2017) presented a framework called “Skill Robot Library” (SRL). This framework has the authority to store key points of the route. In fact, this robot can store user’s behavior in information retrieval, and it will work based on this stored behavior. Behan and O'Keeffe (2009) designed as a mobile robotic assistant, called “LUCAS” for the University of Limerick. This assistant is a help system that supports users intellectually. Kim and Kohtaro (2009) tried to provide robots based on the structured data. This study introduces a conventional and intelligent environment for a librarian robot. This environment is based on RFID technology for these systems. In another study, reference services were investigated using the instant messaging (IM) smart robot. The Shanghai Jiaotong University Library is presented for example. This library provides the IM robot’s intelligent library service using BotPlatform ( Yi et al. , 2011 ).

8. Conclusion

A review of the articles shows that we can use expert and intelligence systems in different library activities and information services. The main goal is to provide specialized services with the help of librarians and information resources specialists. Library services include technical and public services. Both categories use intelligent systems and ESs. These activities include the provision of information resources, the organization of information resources such as classification, indexing, and abstracting, the storage and retrieval of information from library systems, reference services, and circulation desk. We classified the scopes of the researches into four classes “technology,” “user,” “service,” and “resource.” A review of the articles shows that users’ information behavior is a very good way to design intelligent systems. The storing information in cloud and non-cloud spaces allow for the development of these systems. In big data and social networks where scientific information resources are exchanged, intelligent agents can play an important role. User profiles can be a good source for designing ES algorithms based on user knowledge. ESs are the most useable intelligent system in library and information science, which mimic librarian expert’s behaviors to support decision and management. However, individually using this technology is reduced in recent studies. Most information systems have a closed relation with the knowledge and opinion of experts. Using ES technologies such as inference engine and fuzzy rule base may increase the accuracy of them. Therefore, the current information systems can be improved by integration with ES technologies. The librarian robot can reduce the usual and repetitive activities on library shelves. Almost the third of the articles in Emerald Insight in ES have related to the “user” scope, and in librarian robot (18% in WoS), most of the articles have related to the "service" in Emerald Insight and in WoS as well. One of our conclusion is that we need to further research in the area of smart resources.

AI Coverage ( Lancaster, 1997 )

ES elements

The scope of the articles in the field of ES/AI and library (WoS)

The scope of the articles in the field of library and robot (WoS)

The scope of the articles in field ES/AI and Robot in the library (Emerald Insight)

Relative frequencies across the main categories in the abstracts of the articles and thematic interaction between them in the field of ES/AI and robot and library (Emerald Insight)

Relative frequencies across the main categories in the keywords of the articles and thematic interaction between them in the field of ES/AI and robot and library (Emerald Insight)

Articles related to ES/AI and library (WoS)

Articles related to library and robot (WoS)

Articles related to ES/AI and Robot in the library (Emerald Insight)

https://www.emerald.com/insight

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Acknowledgements

This research has been supported by the “Project no. NKFIH-869-4/2019 has been implemented with the support provided from the National Research, Development and Innovation Fund of Hungary, financed under the Tématerületi Kiválósági Program 2019 funding scheme.”

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