Journal of evaluation in clinical practice
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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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Despite the great promises that artificial intelligence (AI) holds for health care, the uptake of such technologies into medical practice is slow. In this paper, we focus on the epistemological issues arising from the development and implementation of a class of AI for clinical practice, namely clinical decision support systems (CDSS). We will first provide an overview of the epistemic tasks of medical professionals, and then analyse which of these tasks can be supported by CDSS, while also explaining why some of them should remain the territory of human experts. ⋯ In practice, this means that the system indicates what factors contributed to arriving at an advice, allowing the user (clinician) to evaluate whether these factors are medically plausible and applicable to the patient. Finally, we defend that proper implementation of CRSS allows combining human and artificial intelligence into hybrid intelligence, were both perform clearly delineated and complementary empirical tasks. Whereas CRSSs can assist with statistical reasoning and finding patterns in complex data, it is the clinicians' task to interpret, integrate and contextualize.
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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 COVID-19 pandemic has impacted every facet of society, including medical research. This paper is the second part of a series of articles that explore the intricate relationship between the different challenges that have hindered biomedical research and the generation of novel scientific knowledge during the COVID-19 pandemic. In the first part of this series, we demonstrated that, in the context of COVID-19, the scientific community has been faced with numerous challenges with respect to (1) finding and prioritizing relevant research questions and (2) choosing study designs that are appropriate for a time of emergency. ⋯ The COVID-19 pandemic presented challenges in terms of (3) evaluating evidence for the purpose of making evidence-based decisions and (4) sharing scientific findings with the rest of the scientific community. This second paper demonstrates that the four challenges outlined in the first and second papers have often compounded each other and have contributed to slowing down the creation of novel scientific knowledge during the COVID-19 pandemic.
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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.