Journal of evaluation in clinical practice
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Artificial intelligence and big data are more and more used in medicine, either in prevention, diagnosis or treatment, and are clearly modifying the way medicine is thought and practiced. Some authors argue that the use of artificial intelligence techniques to analyze big data would even constitute a scientific revolution, in medicine as much as in other scientific disciplines. Moreover, artificial intelligence techniques, coupled with mobile health technologies, could furnish a personalized medicine, adapted to the individuality of each patient. In this paper we argue that this conception is largely a myth: what health professionals and patients need is not more data, but data that are critically appraised, especially to avoid bias. ⋯ The large amount of data thus appears rather as a problem than a solution. What contemporary medicine needs is not more data or more algorithms, but a critical appraisal of the data and of the analysis of the data. Considering the history of epidemiology, we propose three research priorities concerning the use of artificial intelligence and big data in medicine.
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How to classify the human condition? This is one of the main problems psychiatry has struggled with since the first diagnostic systems. The furore over the recent editions of the diagnostic systems DSM-5 and ICD-11 has evidenced it to still pose a wicked problem. ⋯ The promises of AI for mental disorders are threatened by the unmeasurable aspects of mental disorders, and for this reason the use of AI may lead to ethically and practically undesirable consequences in its effective processing. We consider such novel and unique questions AI presents for mental health disorders in detail and evaluate potential novel, AI-specific, ethical implications.
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Transdisciplinary research and generalist practice both face the task of integrating and discerning the value of knowledge across disciplinary and sectoral knowledge cultures. Transdisciplinarity and generalism also both offer philosophical and practical insights into the epistemology, ontology, axiology, and logic of seeing the 'whole'. Although generalism is a skill that can be used in many settings from industry to education, the focus of this paper is the literature of the primary care setting (i.e., general practice or family medicine). Generalist philosophy and practice in the family medicine setting highly values whole person care that uses integrative and interpretive wisdom to include both biomedical and biographical forms of knowledge. Generalist researchers are often caught between reductionist (positivist) biomedical measures and social science (post-positivist) constructivist theories of knowing. Neither of these approaches, even when juxtaposed in mixed-methods research, approximate the complexity of the generalist clinical encounter. A theoretically robust research methodology is needed that acknowledges the complexity of interpreting these ways of knowing in research and clinical practice. ⋯ The concurrence between these approaches to knowing is offered here as Transdisciplinary Generalism - a coherent epistemology for both primary care researchers and generalist clinicians to understand, enact, and research their own sophisticated craft of managing diverse forms of knowledge.
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The goals of learning health systems (LHS) and of AI in medicine overlap in many respects. Both require significant improvements in data sharing and IT infrastructure, aim to provide more personalized care for patients, and strive to break down traditional barriers between research and care. However, the defining features of LHS and AI diverge when it comes to the people involved in medicine, both patients and providers. ⋯ LHS also encourage better coordination of specialists across the health system, but AI aims to replace many specialists with technology and algorithms. This paper argues that these points of conflict may require a reconsideration of the role of humans in medical decision making. Although it is currently unclear to what extent machines will replace humans in healthcare, the parallel development of LHS and AI raises important questions about the exact role for humans within AI-enabled healthcare.
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The diversity of types of evidence (eg, case reports, animal studies and observational studies) makes the assessment of a drug's safety profile into a formidable challenge. While frequentist uncertain inference struggles in aggregating these signals, the more flexible Bayesian approaches seem better suited for this quest. Artificial Intelligence (AI) offers great promise to these approaches for information retrieval, decision support, and learning probabilities from data. ⋯ Properly applied, AI can help the transition of philosophical principles and considerations concerning evidence aggregation for drug safety to a tool that can be used in practice.