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  1. Non-empirical methods for ethics research on digital technologies in medicine, health care and public health: a systematic journal review.Frank Ursin, Regina Müller, Florian Funer, Wenke Liedtke, David Renz, Svenja Wiertz & Robert Ranisch - 2024 - Medicine, Health Care and Philosophy 27 (4):513-528.
    Bioethics has developed approaches to address ethical issues in health care, similar to how technology ethics provides guidelines for ethical research on artificial intelligence, big data, and robotic applications. As these digital technologies are increasingly used in medicine, health care and public health, thus, it is plausible that the approaches of technology ethics have influenced bioethical research. Similar to the “empirical turn” in bioethics, which led to intense debates about appropriate moral theories, ethical frameworks and meta-ethics due to the increased (...)
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  • Exploring value dilemmas of brain monitoring technology through speculative design scenarios.Martha Risnes, Erik Thorstensen, Peyman Mirtaheri & Arild Berg - 2024 - Journal of Responsible Technology 17 (C):100074.
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  • Artificial intelligence paternalism.Ricardo Diaz Milian & Anirban Bhattacharyya - 2023 - Journal of Medical Ethics 49 (3):183-184.
    In response to Ferrario _et al_’s 1 work entitled ‘Ethics of the algorithmic prediction of goal of care preferences: from theory to practice’, we would like to point out an area of concern: the risk of artificial intelligence (AI) paternalism in their proposed framework. Accordingly, in this commentary, we underscore the importance of the implementation of safeguards for AI algorithms before they are deployed in clinical practice. The goal of documenting a living will and advanced directives is to convey personal (...)
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  • Explainability, Public Reason, and Medical Artificial Intelligence.Michael Da Silva - 2023 - Ethical Theory and Moral Practice 26 (5):743-762.
    The contention that medical artificial intelligence (AI) should be ‘explainable’ is widespread in contemporary philosophy and in legal and best practice documents. Yet critics argue that ‘explainability’ is not a stable concept; non-explainable AI is often more accurate; mechanisms intended to improve explainability do not improve understanding and introduce new epistemic concerns; and explainability requirements are ad hoc where human medical decision-making is often opaque. A recent ‘political response’ to these issues contends that AI used in high-stakes scenarios, including medical (...)
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