Limits of trust in medical AI
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
- 2016The Mythos of Model Interpretabilitycited by 716
- 2018Deep Learning: A Critical Appraisalcited by 214
- 2015Massively Multitask Networks for Drug Discoverycited by 49
Built on by
Citing papers per year: 2025: 6; 2026: 1.
7 papers.
- 2025A moving target in AI-assisted decision-making: Dataset shift, model updating, and the problem of update opacitycited by 3
- 2025Diachronic and synchronic variation in the performance of adaptive machine learning systems: The ethical challengescited by 3
- 2025Are clinicians ethically obligated to disclose their use of medical machine learning systems to patients?cited by 1
- 2025Federated learning, ethics, and the double black box problem in medical AIcited by 1
- 2025In defence of post-hoc explanations in medical AIcited by 1
- 2025Explainability of Algorithmscited by 0
- 2026Principles and Guidelines for Randomized Controlled Trials in AI Evaluationcited by 0
- 2026Principles and Guidelines for Randomized Controlled Trials in AI Evaluation3 May 2026
- 2025Explainability of Algorithms19 Aug 2025
- 2025In defence of post-hoc explanations in medical AI29 Apr 2025
- 2025Federated learning, ethics, and the double black box problem in medical AI29 Apr 2025
- 2025Diachronic and synchronic variation in the performance of adaptive machine learning systems: The ethical challenges11 Apr 2025
- 2025A moving target in AI-assisted decision-making: Dataset shift, model updating, and the problem of update opacity7 Apr 2025
- 2025Are clinicians ethically obligated to disclose their use of medical machine learning systems to patients?31 Mar 2025