On the ethics of algorithmic decision-making in healthcare

By Thomas Grote, Philipp Berens. Journal of Medical Ethics, 2019

398 citations recorded by the source corpus.

In recent years, a plethora of high-profile scientific publications has been reporting about machine learning algorithms outperforming clinicians in medical diagnosis or treatment recommendations. This has spiked interest in deploying relevant algorithms with the aim of enhancing decision-making in healthcare. In this paper, we argue that instead of straightforwardly enhancing the decision-making capabilities of clinicians and healthcare institutions, deploying machines learning algorithms entails trade-offs at the epistemic and the normative level. Whereas involving machine learning might improve the accuracy of medical diagnosis, it comes at the expense of opacity when trying to assess the reliability of given diagnosis. Drawing on literature in social epistemology and moral responsibility, we argue that the uncertainty in question potentially undermines the epistemic authority of clinicians. Furthermore, we elucidate potential pitfalls of involving machine learning in healthcare with respect to paternalism, moral responsibility and fairness. At last, we discuss how the deployment of machine learning algorithms might shift the evidentiary norms of medical diagnosis. In this regard, we hope to lay the grounds for further ethical reflection of the opportunities and pitfalls of machine learning for enhancing decision-making in healthcare.

Subfields: General Health Professions, Health Informatics, Public Health, Environmental and Occupational Health

View Paper: https://www.semanticscholar.org/paper/99b43d6dd6923e94b304fe294f43be159630f628

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In General Health Professions

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Citations in the corpus

Chatbot for Health Care and Oncology Applications Using Artificial Intelligence and Machine Learning: Systematic Review (2021, neutral): “With the increased use of diagnostic chatbots, the risk of overconfidence and overtreatment may cause more harm than benefit [99].”
Ethical and regulatory challenges of AI technologies in healthcare: A narrative review (2024, background): “While oﬀering individuals more opportunities to share data and access autonomous health advice might enhance their sense of empowerment and self-care, it could also potentially trigger anxiety and fatigue [93].”
Ethics and governance of trustworthy medical artificial intelligence (2023, background): “People cannot explain how the machine makes judgments [38].”
Explainable, trustworthy, and ethical machine learning for healthcare: A survey (2021, background): “Researchers developing the algorithms can also be responsible for bad decisions [174].”
A survey of algorithmic recourse: definitions, formulations, solutions, and prospects (2020, background): “…above are prevalent not only in finance [20, 133] but also in justice (e.g., pretrial bail) [10], healthcare (e.g., prescription of life-altering medication) [24, 27, 72], and other settings (e.g., hiring) [43, 134, 160] broadly classified as consequential decision-making settings [20, 34, 45, 91].”