mlsanityData to 7 Oct 2026

Readying Medical Students for Medical AI: The Need to Embed AI Ethics Education

arXiv 2109.02866 ↗First posted 7 Sep 2021cs.AIcited by 3

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Abstract (arXiv)

Medical students will almost inevitably encounter powerful medical AI systems early in their careers. Yet, contemporary medical education does not adequately equip students with the basic clinical proficiency in medical AI needed to use these tools safely and effectively. Education reform is urgently needed, but not easily implemented, largely due to an already jam-packed medical curricula. In this article, we propose an education reform framework as an effective and efficient solution, which we call the Embedded AI Ethics Education Framework. Unlike other calls for education reform to accommodate AI teaching that are more radical in scope, our framework is modest and incremental. It leverages existing bioethics or medical ethics curricula to develop and deliver content on the ethical issues associated with medical AI, especially the harms of technology misuse, disuse, and abuse that affect the risk-benefit analyses at the heart of healthcare. In doing so, the framework provides a simple tool for going beyond the "What?" and the "Why?" of medical AI ethics education, to answer the "How?", giving universities, course directors, and/or professors a broad road-map for equipping their students with the necessary clinical proficiency in medical AI.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 3 of its 33 references that are in the map. First posted on arXiv 7 Sep 2021.

Builds on

3 of 33 references.

References · 3

  1. 2020Formalizing Trust in Artificial Intelligence: Prerequisites, Causes and Goals of Human Trust in AIcited by 166
  2. 2017Explainable AI: Beware of Inmates Running the Asylum Or: How I Learnt to Stop Worrying and Love the Social and Behavioural Sciencescited by 84
  3. 2020Trust and Medical AI: The challenges we face and the expertise needed to overcome themcited by 21

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Citing papers per year: 2022: 1; 2023: 1; 2024: 1.

3 papers.