Journal of evaluation in clinical practice
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Medical schools and residency programs have become very adept at teaching medical students and residents an enormous amount of information. However, it is much less clear whether they are effective at fostering virtuous qualities like empathy or professionalism in trainees. This would come as no surprise to Plato, who famously argued in the Meno that virtue cannot be taught. ⋯ As such, we address the question of the teachability of virtue in the realm of medicine, analysing Plato's contradictory analyses in the Meno and Protagoras, and drawing upon modern neuroscience to turn an empirical lens on the question. We explore the ways in which Noddings' Ethic of Care may offer a way forward for medical educators keen to foster virtue in trainees. We conclude by demonstrating how, by harnessing the power of caring relationships, the principles of Noddings' Ethic of Care have already been applied to medical education at a university in Israel.
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This paper aims to show how the focus on eradicating bias from Machine Learning decision-support systems in medical diagnosis diverts attention from the hermeneutic nature of medical decision-making and the productive role of bias. We want to show how an introduction of Machine Learning systems alters the diagnostic process. Reviewing the negative conception of bias and incorporating the mediating role of Machine Learning systems in the medical diagnosis are essential for an encompassing, critical and informed medical decision-making. ⋯ We show that Machine Learning systems join doctors and patients in co-designing a triad of medical diagnosis. We highlight that it is imperative to examine the hermeneutic role of the Machine Learning systems. Additionally, we suggest including not only the patient, but also colleagues to ensure an encompassing diagnostic process, to respect its inherently hermeneutic nature and to work productively with the existing human and machine biases.
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Evidence-based medicine (EBM), one of the most important movements in health care, has been a lightning rod for controversy. Conflicts about the meaning and value of EBM are owing in part to lack of clarity about basic questions regarding its development, the importance of expertise and intuition, and the role of evidence in clinical decision making. These issues have persisted in part because of unclarity at the outset, but also because of how EBM evolved, why it was introduced when it was, and how it was modified following its introduction. ⋯ The paper discusses the impact of this merger, in particular how it led to EBM's identification with managed care and has added momentum to the effort at forging a connection between a normative decision model and clinical judgement. This effort would turn clinical decision making into a conduit for bringing administrative rules and regulations into the consulting room and would result in expertise becoming a surplus skill. The paper closes by discussing a challenge yet unmet by EBM's advocates and critics-to chronicle the dangers that EBM in the framework of DA during the current era of industrialization poses to health and health care, and discover ways of unhinging the relationship between model and judgement.
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In recent years there has been an explosion of interest in Artificial Intelligence (AI) both in health care and academic philosophy. This has been due mainly to the rise of effective machine learning and deep learning algorithms, together with increases in data collection and processing power, which have made rapid progress in many areas. However, use of this technology has brought with it philosophical issues and practical problems, in particular, epistemic and ethical. ⋯ The authors argue that, although effective current or future AI-enhanced EFM may impose an epistemic obligation on the part of clinicians to rely on such systems' predictions or diagnoses as input to SDM, such obligations may be overridden by inherited defeaters, caused by a form of algorithmic bias. The existence of inherited defeaters implies that the duty of care to the client's knowledge extends to any situation in which a clinician (or anyone else) is involved in producing training data for a system that will be used in SDM. Any future AI must be capable of assessing women individually, taking into account a wide range of factors including women's preferences, to provide a holistic range of evidence for clinical decision-making.