mlsanityData to 14 Oct 2026

Are clinicians ethically obligated to disclose their use of medical machine learning systems to patients?

arXiv 2504.01043 ↗First posted 31 Mar 2025cs.CY cs.AI cs.LGcited by 1

Not on the map. Why?

Abstract (arXiv)

It is commonly accepted that clinicians are ethically obligated to disclose their use of medical machine learning systems to patients, and that failure to do so would amount to a moral fault for which clinicians ought to be held accountable. Call this "the disclosure thesis." Four main arguments have been, or could be, given to support the disclosure thesis in the ethics literature: the risk-based argument, the rights-based argument, the materiality argument, and the autonomy argument. In this article, I argue that each of these four arguments are unconvincing, and therefore, that the disclosure thesis ought to be rejected. I suggest that mandating disclosure may also even risk harming patients by providing stakeholders with a way to avoid accountability for harm that results from improper applications or uses of these systems.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 74 references that are in the map. First posted on arXiv 31 Mar 2025.

Builds on

7 of 74 references.

References · 7

  1. 2018Datasheets for Datasetscited by 1,386
  2. 2018Sanity Checks for Saliency Mapscited by 1,156
  3. 2018Model Cards for Model Reportingcited by 1,080
  4. 2019A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cyclecited by 193
  5. 2019Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systemscited by 153
  6. 2020Ethical Machine Learning in Health Carecited by 113
  7. 2025Limits of trust in medical AIcited by 7

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.