mlsanityData to 7 Oct 2026

Limits of trust in medical AI

arXiv 2503.16692 ↗First posted 20 Mar 2025cs.LG cs.AI cs.CYcited by 7

Not on the map. Why?

Abstract (arXiv)

Artificial intelligence (AI) is expected to revolutionize the practice of medicine. Recent advancements in the field of deep learning have demonstrated success in a variety of clinical tasks: detecting diabetic retinopathy from images, predicting hospital readmissions, aiding in the discovery of new drugs, etc. AI's progress in medicine, however, has led to concerns regarding the potential effects of this technology upon relationships of trust in clinical practice. In this paper, I will argue that there is merit to these concerns, since AI systems can be relied upon, and are capable of reliability, but cannot be trusted, and are not capable of trustworthiness. Insofar as patients are required to rely upon AI systems for their medical decision-making, there is potential for this to produce a deficit of trust in relationships in clinical practice.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 3 of its 36 references that are in the map. First posted on arXiv 20 Mar 2025.

Builds on

3 of 36 references.

References · 3

  1. 2016The Mythos of Model Interpretabilitycited by 716
  2. 2018Deep Learning: A Critical Appraisalcited by 214
  3. 2015Massively Multitask Networks for Drug Discoverycited by 49

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 6; 2026: 1.

7 papers.