ArticleJournal of the American Medical Informatics Association : JAMIA2023
Diachronic and synchronic variation in the performance of adaptive machine learning systems: the ethical challenges.
Article in Journal of the American Medical Informatics Association : JAMIA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Why care resists coding: the moral limits of medical AI.BMC medical ethics · 2026Article
- Regulation of artificial intelligence in healthcare: Clinical Laboratory Improvement Amendments (CLIA) as a model.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Ethics of artificial intelligence in embryo assessment: mapping the terrain.Human reproduction (Oxford, England) · 2025Review
- Clearing the Fog: A Scoping Literature Review on the Ethical Issues Surrounding Artificial Intelligence-Based Medical Devices.Journal of personalized medicine · 2024Article
- Bioethics Principles in Machine Learning-Healthcare Application Design: Achieving Health Justice and Health Equity.Harvard public health review (Cambridge, Mass.) · 2024Article
- Utilizing standardized nursing terminologies in implementing an AI-powered fall-prevention tool to improve patient outcomes: a multihospital study.Journal of the American Medical Informatics Association : JAMIA · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesMachine learning (ML) has the potential to facilitate "continual learning" in medicine, in which an ML system continues to evolve in response to exposure to new data over time, even after being deployed in a clinical setting. In this article, we provide a tutorial on the range of ethical issues raised by the use of such "adaptive" ML systems in medicine that have, thus far, been neglected in the literature. TARGET AUDIENCE: The target audiences for this tutorial are the developers of ML AI systems, healthcare regulators, the broader medical informatics community, and practicing clinicians. SCOPE: Discussions of adaptive ML systems to date have overlooked the distinction between 2 sorts of variance that such systems may exhibit-diachronic evolution (change over time) and synchronic variation (difference between cotemporaneous instantiations of the algorithm at different sites)-and underestimated the significance of the latter. We highlight the challenges that diachronic evolution and synchronic variation present for the quality of patient care, informed consent, and equity, and discuss the complex ethical trade-offs involved in the design of such systems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.