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Artificial intelligence (AI) offers opportunities but also challenges for biomedical research and healthcare. This position paper shares the results of the international conference "Fair medicine and AI" (online 3–5 March 2021). Scholars from science and technology studies (STS), gender studies, and ethics of science and technology formulated opportunities, challenges, and research and development desiderata for AI in healthcare. AI systems and solutions, which are being rapidly developed and applied, may have undesirable and unintended consequences including the risk of perpetuating health inequalities for marginalized groups. Socially robust development and implications of AI in healthcare require urgent investigation. There is a particular dearth of studies in human-AI interaction and how this may best be configured to dependably deliver safe, effective and equitable healthcare. To address these challenges, we need to establish diverse and interdisciplinary teams equipped to develop and apply medical AI in a fair, accountable and transparent manner. We formulate the importance of including social science perspectives in the development of intersectionally beneficent and equitable AI for biomedical research and healthcare, in part by strengthening AI health evaluation. • Bias, discrimination and structural injustice for medical AI are an overlooked issue. • AI for biomedical research and healthcare should be beneficent and equitable. • Social science perspectives within AI for medicine development are essential. • Challenges are multifold and an agenda for future research is needed. • Qualitative, ethnographic and participatory approaches could help provide fairer AI. [ABSTRACT FROM AUTHOR]

Medical use cases for machine learning (ML) are growing exponentially. The first hospitals are already using ML systems as decision support systems in their daily routine. At the same time, most ML systems are still opaque and it is not clear how these systems arrive at their predictions. In this paper, we provide a brief overview of the taxonomy of explainability methods and review popular methods. In addition, we conduct a systematic literature search on PubMed to investigate which explainable artificial intelligence (XAI) methods are used in 450 specific medical supervised ML use cases, how the use of XAI methods has emerged recently, and how the precision of describing ML pipelines has evolved over the past 20 years. A large fraction of publications with ML use cases do not use XAI methods at all to explain ML predictions. However, when XAI methods are used, open-source and model-agnostic explanation methods are more commonly used, with SHapley Additive exPlanations (SHAP) and Gradient Class Activation Mapping (Grad-CAM) for tabular and image data leading the way. ML pipelines have been described in increasing detail and uniformity in recent years. However, the willingness to share data and code has stagnated at about one-quarter. XAI methods are mainly used when their application requires little effort. The homogenization of reports in ML use cases facilitates the comparability of work and should be advanced in the coming years. Experts who can mediate between the worlds of informatics and medicine will become more and more in demand when using ML systems due to the high complexity of the domain. [Display omitted] • We estimate that only 16 % of the reported explainability methods could be understood by patients. • The distribution of data types for explainable ML applications for tabular | image | text | audio data is 51 % | 32 % | 3 % | 0 %. • The quality of the description of machine learning pipelines increased in recent years with more homogeneity. • The data and code sharing ratio stagnated in about one quarter. • Most popular explainability methods are SHAP, LIME, and Grad-CAM. [ABSTRACT FROM AUTHOR]

Background: Over the past 30 years, the international conference on Artificial Intelligence in MEdicine (AIME) has been organized at different venues across Europe every 2 years, establishing a forum for scientific exchange and creating an active research community. The Artificial Intelligence in Medicine journal has published theme issues with extended versions of selected AIME papers since 1998.Objectives: To review the history of AIME conferences, investigate its impact on the wider research field, and identify challenges for its future.Methods: We analyzed a total of 122 session titles to create a taxonomy of research themes and topics. We classified all 734 AIME conference papers published between 1985 and 2013 with this taxonomy. We also analyzed the citations to these conference papers and to 55 special issue papers.Results: We identified 30 research topics across 12 themes. AIME was dominated by knowledge engineering research in its first decade, while machine learning and data mining prevailed thereafter. Together these two themes have contributed about 51% of all papers. There have been eight AIME papers that were cited at least 10 times per year since their publication.Conclusions: There has been a major shift from knowledge-based to data-driven methods while the interest for other research themes such as uncertainty management, image and signal processing, and natural language processing has been stable since the early 1990s. AIME papers relating to guidelines and protocols are among the most highly cited. [ABSTRACT FROM AUTHOR]

Summary: Objective: Medical data is often very high dimensional. Depending upon the use, some data dimensions might be more relevant than others. In processing medical data, choosing the optimal subset of features is such important, not only to reduce the processing cost but also to improve the usefulness of the model built from the selected data. This paper presents a data mining study of medical data with fuzzy modeling methods that use feature subsets selected by some indices/methods. Methods: Specifically, three fuzzy modeling methods including the fuzzy k-nearest neighbor algorithm, a fuzzy clustering-based modeling, and the adaptive network-based fuzzy inference system are employed. For feature selection, a total of 11 indices/methods are used. Medical data mined include the Wisconsin breast cancer dataset and the Pima Indians diabetes dataset. The classification accuracy and computational time are reported. To show how good the best performer is, the globally optimal was also found by carrying out an exhaustive testing of all possible combinations of feature subsets with three features. Results: For the Wisconsin breast cancer dataset, the best accuracy of 97.17% was obtained, which is only 0.25% lower than that was obtained by exhaustive testing. For the Pima Indians diabetes dataset, the best accuracy of 77.65% was obtained, which is only 0.13% lower than that obtained by exhaustive testing. Conclusion: This paper has shown that feature selection is important to mining medical data for reducing processing time and for increasing classification accuracy. However, not all combinations of feature selection and modeling methods are equally effective and the best combination is often data-dependent, as supported by the breast cancer and diabetes data analyzed in this paper. [Copyright &y& Elsevier]

Summary: Objective: The main aim of this paper is to propose and discuss promising directions of research in the field of temporal representation and reasoning in medicine, taking into account the recent scientific literature and challenging issues of current interest as viewed from the different research perspectives of the authors of the paper. Background: Temporal representation and reasoning in medicine is a well-known field of research in the medical as well as computer science community. It encompasses several topics, such as summarizing data from temporal clinical databases, reasoning on temporal clinical data for therapeutic assessments, and modeling uncertainty in clinical knowledge and data. It is also related to several medical tasks, such as monitoring intensive care patients, providing treatments for chronic patients, as well as planning and scheduling clinical routine activities within complex healthcare organizations. Methodology: The authors jointly identified significant research areas based on their importance as for temporal representation and reasoning issues; the subjects were considered to be promising topics of future activity. Every subject was addressed in detail by one or two authors and then discussed with the entire team to achieve a consensus about future fields of research. Results: We identified and focused on four research areas, namely (i) fuzzy logic, time, and medicine, (ii) temporal reasoning and data mining, (iii) health information systems, business processes, and time, and (iv) temporal clinical databases. For every area, we first highlighted a few basic notions that would permit any reader—including those who are unfamiliar with the topic—to understand the main goals. We then discuss interesting and promising directions of research, taking into account the recent literature and underlining the yet unresolved medical/clinical issues that deserve further scientific investigation. The considered research areas are by no means disjointed, because they share common theoretical and methodological features. Moreover, subjects of imminent interest in medicine are represented in many of the fields considered. Conclusions: We propose and discuss promising subjects of future research that deserve investigation to develop software systems that will properly manage the multifaceted temporal aspects of information and knowledge encountered by physicians during their clinical work. As the subjects of research have resulted from merging the different perspectives of the authors involved in this study, we hope the paper will succeed in stimulating discussion and multidisciplinary work in the described fields of research. [Copyright &y& Elsevier]

This paper describes the fuzzy knowledge representation framework of the medical computer consultation system MedFrame/CADIAG-IV as well as the specific knowledge acquisition techniques that have been developed to support the definition of knowledge concepts and inference rules. As in its predecessor system CADIAG-II, fuzzy medical knowledge bases are used to model the uncertainty and the vagueness of medical concepts and fuzzy logic reasoning mechanisms provide the basic inference processes. The elicitation and acquisition of medical knowledge from domain experts has often been described as the most difficult and time-consuming task in knowledge-based system development in medicine. It comes as no surprise that this is even more so when unfamiliar representations like fuzzy membership functions are to be acquired. From previous projects we have learned that a user-centered approach is mandatory in complex and ill-defined knowledge domains such as internal medicine. This paper describes the knowledge acquisition framework that has been developed in order to make easier and more accessible the three main tasks of: (a) defining medical concepts; (b) providing appropriate interpretations for patient data; and (c) constructing inferential knowledge in a fuzzy knowledge representation framework. Special emphasis is laid on the motivations for some system design and data modeling decisions. The theoretical framework has been implemented in a software package, the Knowledge Base Builder Toolkit. The conception and the design of this system reflect the need for a user-centered, intuitive, and easy-to-handle tool. First results gained from pilot studies have shown that our approach can be successfully implemented in the context of a complex fuzzy theoretical framework. As a result, this critical aspect of knowledge-based system development can be accomplished more easily. [Copyright &y& Elsevier]

Researchers who design intelligent systems for medical decision support, are aware of the need for response to real clinical issues, in particular the need to address the specific ethical problems that the medical domain has in using black boxes. This means such intelligent systems have to be thoroughly evaluated, for acceptability. Attempts at compliance, however, are hampered by lack of guidelines. This paper addresses the issue of inherent performance evaluation, which researchers have addressed in part, but a Medline search, using neural networks as an example of intelligent systems, indicated that only about 12.5% evaluated inherent performance adequately. This paper aims to address this issue by concentrating on the possible evaluation methodology, giving a framework and specific suggestions for each type of classification problem. This should allow the developers of intelligent systems to produce evidence of a sufficiency of output performance evaluation. [Copyright &y& Elsevier]

Artificial intelligence is a broad field that comprises a wide range of techniques, where deep learning is presently the one with the most impact. Moreover, the medical field is an area where data both complex and massive and the importance of the decisions made by doctors make it one of the fields in which deep learning techniques can have the greatest impact. A systematic review following the Cochrane recommendations with a multidisciplinary team comprised of physicians, research methodologists and computer scientists has been conducted. This survey aims to identify the main therapeutic areas and the deep learning models used for diagnosis and treatment tasks. The most relevant databases included were MedLine, Embase, Cochrane Central, Astrophysics Data System, Europe PubMed Central, Web of Science and Science Direct. An inclusion and exclusion criteria were defined and applied in the first and second peer review screening. A set of quality criteria was developed to select the papers obtained after the second screening. Finally, 126 studies from the initial 3493 papers were selected and 64 were described. Results show that the number of publications on deep learning in medicine is increasing every year. Also, convolutional neural networks are the most widely used models and the most developed area is oncology where they are used mainly for image analysis. [ABSTRACT FROM AUTHOR]

Abstract: Objective: Speed, cost, and accuracy are three important goals in disease diagnosis. This paper proposes a machine learning-based expert system algorithm to optimize these goals and assist diagnostic decisions in a sequential decision-making setting. Methods: The algorithm consists of three components that work together to identify the sequence of diagnostic tests that attains the treatment or no test threshold probability for a query case with adequate certainty: lazy-learning classifiers, confident diagnosis, and locally sequential feature selection (LSFS). Speed-based and cost-based objective functions can be used as criteria to select tests. Results: Results of four different datasets are consistent. All LSFS functions significantly reduce tests and costs. Average cost savings for heart disease, thyroid disease, diabetes, and hepatitis datasets are 50%, 57%, 22%, and 34%, respectively. Average test savings are 55%, 73%, 24%, and 39%, respectively. Accuracies are similar to or better than the baseline (the classifier that uses all available tests in the dataset). Conclusion: We have demonstrated a new approach that dynamically estimates and determines the optimal sequence of tests that provides the most information (or disease probability) based on a patient''s available information. [Copyright &y& Elsevier]

Summary: Objective: This article delineates a relatively unknown path in the history of medical philosophy and medical diagnosis. It is concerned with the phenomenon of vagueness in the physician''s “style of thinking” and with the use of fuzzy sets, systems, and relations with a view to create a model of such reasoning when physicians make a diagnosis. It represents specific features of medical ways of thinking that were mentioned by the Polish physician and philosopher Ludwik Fleck in 1926. The paper links Lotfi Zadeh''s work on system theory before the age of fuzzy sets with system-theory concepts in medical philosophy that were introduced by the philosopher Mario Bunge, and with the fuzzy-theoretical analysis of the notions of health, illness, and disease by the Iranian-German physician and philosopher Kazem Sadegh-Zadeh. Material: Some proposals to apply fuzzy sets in medicine were based on a suggestion made by Zadeh: symptoms and diseases are fuzzy in nature and fuzzy sets are feasible to represent these entity classes of medical knowledge. Yet other attempts to use fuzzy sets in medicine were self-contained. The use of this approach contributed to medical decision-making and the development of computer-assisted diagnosis in medicine. Conclusion: With regard to medical philosophy, decision-making, and diagnosis; the framework of fuzzy sets, systems, and relations is very useful to deal with the absence of sharp boundaries of the sets of symptoms, diagnoses, and phenomena of diseases. The foundations of reasoning and computer assistance in medicine were the result of a rapid accumulation of data from medical research. This explosion of knowledge in medicine gave rise to the speculation that computers could be used for the medical diagnosis. Medicine became, to a certain extent, a quantitative science. In the second half of the 20th century medical knowledge started to be stored in computer systems. To assist physicians in medical decision-making and patient care, medical expert systems using the theory of fuzzy sets and relations (such as the Viennese “fuzzy version” of the Computer-Assisted Diagnostic System, Cadiag, which was developed at the end of the 1970s) were constructed. The development of fuzzy relations in medicine and their application in computer-assisted diagnosis show that this fuzzy approach is a framework to deal with the “fuzzy mode of thinking” in medicine. [Copyright &y& Elsevier]

Summary: Objectives: Today a great many medical schools have turned to a problem-based learning (PBL) approach to teaching. While PBL has many strengths, effective PBL requires the tutor to provide a high degree of personal attention to the students, which is difficult in the current academic environment of increasing demands on faculty time. This paper describes intelligent tutoring in a collaborative medical tutor for PBL. The main contribution of our work is the development of representational techniques and algorithms for generating tutoring hints in PBL group problem solving, as well as the implementation of these techniques in a collaborative intelligent tutoring system, COMET. The system combines concepts from computer-supported collaborative learning with those from intelligent tutoring systems. Methods and materials: The system uses Bayesian networks to model individual student clinical reasoning, as well as that of the group. The prototype system incorporates substantial domain knowledge in the areas of head injury, stroke and heart attack. Tutoring in PBL is particularly challenging since the tutor should provide as little guidance as possible while at the same time not allowing the students to get lost. From studies of PBL sessions at a local medical school, we have identified and implemented eight commonly used hinting strategies. In order to evaluate the appropriateness and quality of the hints generated by our system, we compared the tutoring hints generated by COMET with those of experienced human tutors. We also compared the focus of group activity chosen by COMET with that chosen by human tutors. Results: On average, 74.17% of the human tutors used the same hint as COMET. The most similar human tutor agreed with COMET 83% of the time and the least similar tutor agreed 62% of the time. Our results show that COMET''s hints agree with the hints of the majority of the human tutors with a high degree of statistical agreement (McNemar test, p =0.652, κ =0.773). The focus of group activity chosen by COMET agrees with that chosen by the majority of the human tutors with a high degree of statistical agreement (McNemar test, p =0.774, κ =0.823). Conclusion: Bayesian network clinical reasoning models can be combined with generic tutoring strategies to successfully emulate human tutor hints in group medical PBL. [Copyright &y& Elsevier]

Summary: Objective: This paper proposes a novel method for the extraction and classification of individual motor unit action potentials (MUAPs) from intramuscular electromyographic signals. Methodology: The proposed method automatically detects the number of template MUAP clusters and classifies them into normal, neuropathic or myopathic. It consists of three steps: (i) preprocessing of electromyogram (EMG) recordings, (ii) MUAP detection and clustering and (iii) MUAP classification. Results: The approach has been validated using a dataset of EMG recordings and an annotated collection of MUAPs. The correct identification rate for MUAP clustering is 93, 95 and 92% for normal, myopathic and neuropathic, respectively. Ninety-one percent of the superimposed MUAPs were correctly identified. The obtained accuracy for MUAP classification is about 86%. Conclusion: The proposed method, apart from efficient EMG decomposition addresses automatic MUAP classification to neuropathic, myopathic or normal classes directly from raw EMG signals. [Copyright &y& Elsevier]

Summary: Objective: Mémoire is a framework for sharing and distributing case bases and case-based reasoning (CBR) systems in biology and medicine. Methods and material: This paper first introduces the semantic Web approach to build a better Web where search engines, knowledge sources and servers, applications and services can live, work, and learn in cooperation. This semantic approach is particularly well suited for biomedical domains because significant ontologies have been developed there and constitute a sound basis for the standardization effort required for the semantic Web. Case-based reasoning systems in biomedicine have also benefited from these biomedical ontologies and models. Results: This article demonstrates for three such systems how a semantics infused approach in CBR gives better, more accurate results in CBR. From this previous work on a semantic approach in CBR in biomedicine, the Mémoire framework has evolved. Conclusion: Mémoire proposes a unified OWL-based representation language for cases and case-based ontologies in biomedicine, where a Web Ontology Language (OWL) is a language to represent ontologies on the Web. Mémoire provides a set of tools for building case-based reasoning systems compliant with its language. Mémoire is extensible and can be adapted to different types of biomedical application domains, tasks, and environments. Mémoire will permit bridging the gap within the multiple case-based reasoning systems dedicated to a single domain, and make available to agents and Web services the case-based competency of the CBR systems adopting its interchange language. The approach could be extended to other application domains of CBR. [Copyright &y& Elsevier]

Summary: Objectives: This paper presents current work in case-based reasoning (CBR) in the health sciences, describes current trends and issues, and projects future directions for work in this field. Methods and material: It represents the contributions of researchers at two workshops on case-based reasoning in the health sciences. These workshops were held at the Fifth International Conference on Case-Based Reasoning (ICCBR-03) and the Seventh European Conference on Case-Based Reasoning (ECCBR-04). Results: Current research in CBR in the health sciences is marked by its richness. Highlighted trends include work in bioinformatics, support to the elderly and people with disabilities, formalization of CBR in biomedicine, and feature and case mining. Conclusion: CBR systems are being better designed to account for the complexity of biomedicine, to integrate into clinical settings and to communicate and interact with diverse systems and methods. [Copyright &y& Elsevier]

In recent years, much research has been done on the use of fuzzy systems in medicine. The fuzzy rule-bases have usually been derived after extensive discussion with the clinical experts. This takes a lot of time from the clinical experts and the knowledge engineers. This paper presents the use of the adaptive neuro-fuzzy inference system (ANFIS) in rule-base derivation for ventilator control. The change of the inspired fraction of oxygen (FiO2) advised by eight clinical experts responding to 71 clinical scenarios was recorded. ANFIS and a multilayer perceptron (MLP) were then used to model the relationship between the inputs (the arterial oxygen tension (PaO2), FiO2 and the positive end-expiratory pressure (PEEP) level) and the change in FiO2 suggested. Compared to a previous fuzzy advisor (FAVeM), both the ANFIS and the MLP were found to correlate with the clinicians’ decision better (correlation coefficient of 0.694 and 0.701, respectively compared to 0.630). A formerly developed model-based radial basis network advisor (RBN-MB) was used for comparison. Closed-loop simulations showed that the ANFIS, MLP and the RBN-MB’s performance were comparable to the clinicians’ performance (correlation coefficients of 0.852, 0.962 and 0.787, respectively). The FAVeM’s performance differed from the clinicians’ performance (correlation coefficient of 0.332) but the resulting PaO2 was still within safety limits. [Copyright &y& Elsevier]

In this paper, the current published knowledge about smart and adaptive engineering systems in medicine is reviewed. The achievements of frontier research in this particular field within medical engineering are described. A multi-disciplinary approach to the applications of adaptive systems is observed from the literature surveyed. The three modalities of diagnosis, imaging and therapy are considered to be an appropriate classification method for the analysis of smart systems being applied to specified medical sub-disciplines. It is expected that future research in biomedicine should identify subject areas where more advanced intelligent systems could be applied than is currently evident. The literature provides evidence of hybridisation of different types of adaptive and smart systems with applications in different areas of medical specifications. [Copyright &y& Elsevier]