4,363 research outputs found

    Association of Clinician Diagnostic Performance With Machine Learning-Based Decision Support Systems: A Systematic Review.

    Get PDF
    IMPORTANCE: An increasing number of machine learning (ML)-based clinical decision support systems (CDSSs) are described in the medical literature, but this research focuses almost entirely on comparing CDSS directly with clinicians (human vs computer). Little is known about the outcomes of these systems when used as adjuncts to human decision-making (human vs human with computer). OBJECTIVES: To conduct a systematic review to investigate the association between the interactive use of ML-based diagnostic CDSSs and clinician performance and to examine the extent of the CDSSs' human factors evaluation. EVIDENCE REVIEW: A search of MEDLINE, Embase, PsycINFO, and grey literature was conducted for the period between January 1, 2010, and May 31, 2019. Peer-reviewed studies published in English comparing human clinician performance with and without interactive use of an ML-based diagnostic CDSSs were included. All metrics used to assess human performance were considered as outcomes. The risk of bias was assessed using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Risk of Bias in Non-Randomised Studies-Intervention (ROBINS-I). Narrative summaries were produced for the main outcomes. Given the heterogeneity of medical conditions, outcomes of interest, and evaluation metrics, no meta-analysis was performed. FINDINGS: A total of 8112 studies were initially retrieved and 5154 abstracts were screened; of these, 37 studies met the inclusion criteria. The median number of participating clinicians was 4 (interquartile range, 3-8). Of the 107 results that reported statistical significance, 54 (50%) were increased by the use of CDSSs, 4 (4%) were decreased, and 49 (46%) showed no change or an unclear change. In the subgroup of studies carried out in representative clinical settings, no association between the use of ML-based diagnostic CDSSs and improved clinician performance could be observed. Interobserver agreement was the commonly reported outcome whose change was the most strongly associated with CDSS use. Four studies (11%) reported on user feedback, and, in all but 1 case, clinicians decided to override at least some of the algorithms' recommendations. Twenty-eight studies (76%) were rated as having a high risk of bias in at least 1 of the 4 QUADAS-2 core domains, and 6 studies (16%) were considered to be at serious or critical risk of bias using ROBINS-I. CONCLUSIONS AND RELEVANCE: This systematic review found only sparse evidence that the use of ML-based CDSSs is associated with improved clinician diagnostic performance. Most studies had a low number of participants, were at high or unclear risk of bias, and showed little or no consideration for human factors. Caution should be exercised when estimating the current potential of ML to improve human diagnostic performance, and more comprehensive evaluation should be conducted before deploying ML-based CDSSs in clinical settings. The results highlight the importance of considering supported human decisions as end points rather than merely the stand-alone CDSSs outputs

    Information Systems and Healthcare XXXIV: Clinical Knowledge Management Systems—Literature Review and Research Issues for Information Systems

    Get PDF
    Knowledge Management (KM) has emerged as a possible solution to many of the challenges facing U.S. and international healthcare systems. These challenges include concerns regarding the safety and quality of patient care, critical inefficiency, disparate technologies and information standards, rapidly rising costs and clinical information overload. In this paper, we focus on clinical knowledge management systems (CKMS) research. The objectives of the paper are to evaluate the current state of knowledge management systems diffusion in the clinical setting, assess the present status and focus of CKMS research efforts, and identify research gaps and opportunities for future work across the medical informatics and information systems disciplines. The study analyzes the literature along two dimensions: (1) the knowledge management processes of creation, capture, transfer, and application, and (2) the clinical processes of diagnosis, treatment, monitoring and prognosis. The study reveals that the vast majority of CKMS research has been conducted by the medical and health informatics communities. Information systems (IS) researchers have played a limited role in past CKMS research. Overall, the results indicate that there is considerable potential for IS researchers to contribute their expertise to the improvement of clinical process through technology-based KM approaches

    An overview of decision table literature 1982-1995.

    Get PDF
    This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.

    MACHINE LEARNING APPROACHES ALONG THE RADIOLOGY VALUE CHAIN – RETHINKING VALUE PROPOSITIONS

    Get PDF
    Radiology is experiencing an increased interest in machine learning with its ability to use a large amount of available data. However, it remains unclear how and to what extent machine learning will affect radiology businesses. Conducting a systematic literature review and expert interviews, we compile the opportunities and challenges of machine learning along the radiology value chain to discuss their implications for the radiology business. Machine learning can improve diagnostic quality by reducing human errors, accurately analysing large amounts of data, quantifying reports, and integrating data. Hence, it strengthens radiology businesses seeking product or service leadership. Machine learning fosters efficiency by automating accompanying activities such as generating study protocols or reports, avoiding duplicate work due to low image quality, and supporting radiologists. These efficiency improvements advance the operational excellence strategy. By providing personnel and proactive medical solutions beyond the radiology silo, machine learning supports a customer intimacy strategy. However, the opportunities face challenges that are technical (i.e., lack of data, weak labelling, and generalisation), legal (i.e., regulatory approval and privacy laws), and persuasive (i.e., radiologists’ resistance and patients’ distrust). Our findings shed light on the strategic positioning of radiology businesses, contributing to academic discourse and practical decision-making

    Computer-aided DSM-IV-diagnostics – acceptance, use and perceived usefulness in relation to users' learning styles

    Get PDF
    BACKGROUND: CDSS (computerized decision support system) for medical diagnostics have been studied for long. This study was undertaken to investigate how different preferences of Learning Styles (LS) of psychiatrists might affect acceptance, use and perceived usefulness of a CDSS for diagnostics in psychiatry. METHODS: 49 psychiatrists (specialists and non-specialists) from 3 different clinics volunteered to participate in this study and to use the CDSS to diagnose a paper-based case (based on a real patient). LS, attitudes to CDSS and complementary data were obtained via questionnaires and interviews. To facilitate the study, a special version of the CDSS was created, which automatically could log interaction details. RESULTS: The LS preferences (according to Kolb) of the 49 physicians turned out as follows: 37% were Assimilating, 31% Converging, 27% Accommodating and 6% Diverging. The CDSS under study seemed to favor psychiatrists with abstract conceptualization information perceiving mode (Assimilating and Converging learning styles). A correlation between learning styles preferences and computer skill was found. Positive attitude to computer-aided diagnostics and learning styles preferences was also found to correlate. Using the CDSS, the specialists produced only 1 correct diagnosis and the non-specialists 2 correct diagnoses (median values) as compared to the three predetermined correct diagnoses of the actual case. Only 10% had all three diagnoses correct, 41 % two correct, 47 % one correct and 2 % had no correct diagnose at all. CONCLUSION: Our results indicate that the use of CDSS does not guarantee correct diagnosis and that LS might influence the results. Future research should focus on the possibility to create systems open to individuals with different LS preferences and possibility to create CDSS adapted to the level of expertise of the user

    Artificial Intelligence in Medicine and Healthcare: applications, availability and societal impact

    Get PDF
    This report reviews and classifies the current and near-future applications of Artificial Intelligence (AI) in Medicine and Healthcare according to their ethical and societal impact and the availability level of the various technological implementations. It provides conceptual foundations for well-informed policy-oriented work, research, and forward-looking activities that address the opportunities and challenges created in the field of AI in Medicine and Healthcare. This report is aimed for policy developers, but it also makes contributions that are of interest for researchers studying the impact and the future of AI on Healthcare, for scientific and technological stakeholders in this field and for the general public. This report is based on an analysis of the state of the art of research and technology, including software, personal monitoring devices, genetic tests and editing tools, personalized digital models, online platforms, augmented reality devices, and surgical and companion robotics. From this analysis, it is presented the concept of “extended personalized medicine”, and it is explored the public perception of medical AI systems, and how they show, simultaneously, extraordinary opportunities and drawbacks. In addition, this report addresses the transformation of the roles of doctors and patients in an age of ubiquitous information and identifies three main paradigms in AI-supported Medicine: “fake-based”, “patient-generated”, and “scientifically tailored” views. This Report presents: - An updated overview of the many aspects related to the social impact of Artificial Intelligence and its applications in Medicine and Health. A new ‘Technology Availability Scale’ is defined to evaluate and compare their current status. - Recent examples of the growing social concerns and debates in the general press, social media and other web-bases sources. - A ‘Visual Overview of AI and AI-mediated technologies in Medicine and Healthcare’, in which two figures show, respectively, a (newly proposed) classification according to their ethical and social impact, and the most relevant ethical and social aspects considered for such classification. Some key questions, controversies, significant, and conflicting issues are outlined for each aspect. - A ‘Structured Overview’, with a sorted list of technologies and their implementations, including perspectives, conflicting views and potential pitfalls, and a corresponding, extensive list of references. - A conclusive set of policy challenges, namely the need of informed citizens, key aspects (of AI and AI-mediated technologies in Medicine and Healthcare) to evaluate, and some recommendations towards a European leadership in this sector. - We finally relate our study with an update on the use of AI technologies to fight the SARS-CoV-2 virus and COVID-19 pandemic disease.JRC.A.5-Scientific Developmen

    Artificial Intelligence in Medicine and Healthcare: applications, availability and societal impact

    Get PDF
    Comisión Europea. Joint Research Centre. Serie: JRC Science for Police ReportThis report reviews and classifies the current and near-future applications of Artificial Intelligence (AI) in Medicine and Healthcare according to their ethical and societal impact and the availability level of the various technological implementations. It provides conceptual foundations for well-informed policy-oriented work, research, and forward-looking activities that address the opportunities and challenges created in the field of AI in Medicine and Healthcare. This report is aimed for policy developers, but it also makes contributions that are of interest for researchers studying the impact and the future of AI on Healthcare, for scientific and technological stakeholders in this field and for the general public.This report is based on an analysis of the state of the art of research and technology, including software, personal monitoring devices, genetic tests and editing tools, personalized digital models, online platforms, augmented reality devices, and surgical and companion robotics. From this analysis, it is presented the concept of “extended personalized medicine”, and it is explored the public perception of medical AI systems, and how they show, simultaneously, extraordinary opportunities and drawbacks. In addition, this report addresses the transformation of the roles of doctors and patients in an age of ubiquitous information and identifies three main paradigms in AI-supported Medicine: “fake-based”, “patient-generated”, and “scientifically tailored” views.This Report presents:- An updated overview of the many aspects related to the social impact of Artificial Intelligence and its applications in Medicine and Health. A new ‘Technology Availability Scale’ is defined to evaluate and compare their current status.- Recent examples of the growing social concerns and debates in the general press, social media and other web-bases sources.- A ‘Visual Overview of AI and AI-mediated technologies in Medicine and Healthcare’, in which two figures show, respeComisión Europea. Joint Research Centr

    Machine learning and its applications in reliability analysis systems

    Get PDF
    In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

    Advancing automation and robotics technology for the Space Station Freedom and for the US economy

    Get PDF
    The progress made by levels 1, 2, and 3 of the Office of Space Station in developing and applying advanced automation and robotics technology is described. Emphasis is placed upon the Space Station Freedom Program responses to specific recommendations made in the Advanced Technology Advisory Committee (ATAC) progress report 10, the flight telerobotic servicer, and the Advanced Development Program. Assessments are presented for these and other areas as they apply to the advancement of automation and robotics technology for the Space Station Freedom
    corecore