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  1. Privacy and artificial intelligence: challenges for protecting health information in a new era.Blake Murdoch - 2021 - BMC Medical Ethics 22 (1):1-5.
    BackgroundAdvances in healthcare artificial intelligence (AI) are occurring rapidly and there is a growing discussion about managing its development. Many AI technologies end up owned and controlled by private entities. The nature of the implementation of AI could mean such corporations, clinics and public bodies will have a greater than typical role in obtaining, utilizing and protecting patient health information. This raises privacy issues relating to implementation and data security. Main bodyThe first set of concerns includes access, use and control (...)
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  • (1 other version)What Evidence in Evidence‐Based Medicine?John Worrall - 2002 - Philosophy of Science 69 (S3):S316-S330.
    Evidence-Based Medicine is a relatively new movement that seeks to put clinical medicine on a firmer scientific footing. I take it as uncontroversial that medical practice should be based on best evidence—the interesting questions concern the details. This paper tries to move towards a coherent and unified account of best evidence in medicine, by exploring in particular the EBM position on RCTs.
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  • The challenges of evidence-based medicine: A philosophical perspective.Abhaya V. Kulkarni - 2005 - Medicine, Health Care and Philosophy 8 (2):255-260.
    Although evidence-based medicine (EBM) has gained prominence in current medical practice and research, it has also had to deal with a number of problems and inconsistencies. For example, how do clinicians reconcile discordant results of randomized trials or how do they apply results of randomized trials to individual patients? In an attempt to examine such problems in a structured way, this essay describes EBM within a philosophical framework of science. Using this approach, some of the problems and challenges faced by (...)
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  • Beyond generalization: a theory of robustness in machine learning.Thomas Grote & Timo Freiesleben - 2023 - Synthese 202 (4):1-28.
    The term robustness is ubiquitous in modern Machine Learning (ML). However, its meaning varies depending on context and community. Researchers either focus on narrow technical definitions, such as adversarial robustness, natural distribution shifts, and performativity, or they simply leave open what exactly they mean by robustness. In this paper, we provide a conceptual analysis of the term robustness, with the aim to develop a common language, that allows us to weave together different strands of robustness research. We define robustness as (...)
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  • The myth and fallacy of simple extrapolation in medicine.Jonathan Fuller - 2019 - Synthese 198 (4):2919-2939.
    Simple extrapolation is the orthodox approach to extrapolating from clinical trials in evidence-based medicine: extrapolate the relative effect size from the trial unless there is a compelling reason not to do so. I argue that this method relies on a myth and a fallacy. The myth of simple extrapolation is the idea that the relative risk is a ‘golden ratio’ that is usually transportable due to some special mathematical or theoretical property. The fallacy of simple extrapolation is an unjustified argument (...)
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  • (1 other version)What evidence in evidence-based medicine?John Worrall - 2002 - Proceedings of the Philosophy of Science Association 2002 (3):S316-S330.
    Evidence-Based Medicine is a relatively new movement that seeks to put clinical med- icine on a firmer scientific footing. I take it as uncontroversial that medical practice should be based on best evidence-the interesting questions concern the details. This paper tries to move towards a coherent and unified account of best evidence in medicine, by exploring in particular the EBM position on RCTs (randomized controlled trials).
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  • Defining the undefinable: the black box problem in healthcare artificial intelligence.Jordan Joseph Wadden - 2022 - Journal of Medical Ethics 48 (10):764-768.
    The ‘black box problem’ is a long-standing talking point in debates about artificial intelligence. This is a significant point of tension between ethicists, programmers, clinicians and anyone else working on developing AI for healthcare applications. However, the precise definition of these systems are often left undefined, vague, unclear or are assumed to be standardised within AI circles. This leads to situations where individuals working on AI talk over each other and has been invoked in numerous debates between opaque and explainable (...)
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  • Patterns of Discovery.Norwood R. Hanson, A. D. Ritchie & Henryk Mehlberg - 1960 - British Journal for the Philosophy of Science 10 (40):346-349.
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  • Randomized Controlled Trials in Medical AI.Konstantin Genin & Thomas Grote - 2021 - Philosophy of Medicine 2 (1).
    Various publications claim that medical AI systems perform as well, or better, than clinical experts. However, there have been very few controlled trials and the quality of existing studies has been called into question. There is growing concern that existing studies overestimate the clinical benefits of AI systems. This has led to calls for more, and higher-quality, randomized controlled trials of medical AI systems. While this a welcome development, AI RCTs raise novel methodological challenges that have seen little discussion. We (...)
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