Ethical Machine Learning in Healthcare
Open accessAbstract
The use of machine learning (ML) in healthcare raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical considerations for equitable ML in the advancement of healthcare. Specifically, we frame ethics of ML in healthcare through the lens of social justice. We describe ongoing efforts and outline challenges in a proposed pipeline of ethical ML in health, ranging from problem selection to postdeployment considerations. We close by summarizing recommendations to address these challenges.
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Memory raises significant concerns regarding privacy, data management, and bias.
“At the same time, memory also raises significant concerns regarding privacy, data management, and bias [17].”
From Medical AI Agents for Clinical Decision Support: Viewpoint Using the Planning, Action, Reflection, and Memory (PARM) Analytical Lens (2026) · cites this paper as [17]
The Data Collection section discusses bias in health data, and its Population-Specific Data Losses subsection describes how patient-privacy protections can introduce statistical bias (page unavailable). These passages support concerns about privacy and bias in healthcare data, but they do not say that memory raises those concerns or address memory-related data management. (LLM, conf=0.82)
Experimental — accuracy not yet measuredA wide variety of governance best practices in the form of frameworks, governance structures, checklists, and guidelines have been proposed.
“As AI solutions have emerged, a wide variety of governance best practices in the form of frameworks, governance structures, checklists, and guidelines have been proposed7–23.”
From Advancing healthcare AI governance through a comprehensive maturity model based on systematic review (2026) · cites this paper as [9]
The Regulation and Model and Data Documentation sections describe proposed frameworks and checklists for healthcare ML. They do not establish the claimed range of proposed governance structures and guidelines; the Regulation section instead says comprehensive guidance for healthcare model research and deployment does not yet exist. Page numbers are unavailable in our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measuredA recurring theme across multiple articles was the necessity for an overarching governance body to assist in selecting AI tools and guide validation, implementation, and monitoring processes.
“A recurring theme across multiple articles was the necessity for an overarching governance body7–10,17. This entity would not only assist in selecting AI tools and software for implementation but also guide the validation, implementation, and monitoring processes.”
From Advancing healthcare AI governance through a comprehensive maturity model based on systematic review (2026) · cites this paper as [9]
The Postdeployment Considerations > Regulation section says the FDA regulates healthcare ML models and calls for a comprehensive framework to audit and regulate them, including postmarket evaluation. It does not call for an overarching governance body that selects AI tools or guides their validation and implementation. The indexed passages provide no page numbers. (LLM, conf=0.82)
Experimental — accuracy not yet measuredMost articles briefly addressed the importance of clear objectives for AI implementation in healthcare, with only a few offering actionable guidance.
“Most articles briefly addressed the importance of clear objectives for AI implementation in healthcare, with only a few offering actionable guidance9,11,14,17,21.”
From Advancing healthcare AI governance through a comprehensive maturity model based on systematic review (2026) · cites this paper as [9]
The Recommendations section says researchers should reflect on a model’s goals and articulate them in a preanalysis plan, supporting the importance of clear objectives. It does not assess whether most articles address objectives only briefly or whether few offer actionable guidance. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAI governance frameworks specifically designed for healthcare largely emphasize high-level ethical principles or focus on technical considerations related to algorithm development, implementation, and maintenance.
“However, these frameworks largely emphasize high-level ethical principles30–32 or focus on technical considerations related to algorithm development, implementation, and maintenance33,34.”
From Establishing organizational AI governance in healthcare: a case study in Canada (2025) · cites this paper as [31]
The Algorithm Development and Postdeployment Considerations sections discuss technical choices, deployment, and ongoing model audits, supporting this paper as an example of work addressing those stages. But the Introduction and Problem Selection section also address social justice, research funding, and whose health needs receive attention; describing the paper as focused on technical considerations alone omits that scope. The paper does not establish what healthcare AI governance frameworks largely emphasize as a group. (LLM, conf=0.84)
Experimental — accuracy not yet measured
CF receives over 3 times more funding per affected individual from the US National Institutes of Health compared to SCD, as well as hundreds of times more private funding.
“Nevertheless, CF receives over 3 times more funding per affected individual from the US National Institutes of Health, compared to that for SCD, as well as hundreds of times more private funding [22].”
From Bias in artificial intelligence algorithms and recommendations for mitigation (2023) · cites this paper as [22]
Countries such as Canada and France do not record race and ethnicity in their national health databases.
“For example, countries such as Canada and France do not record race and ethnicity in their national health databases”
From Bias in artificial intelligence algorithms and recommendations for mitigation (2023) · cites this paper as [22]
These deficiencies cast considerable doubt on models' usefulness and safety, and raises concerns about their potential to create or widen healthcare disparities.
“These deficiencies cast considerable doubt on models’ usefulness and safety, and raises concerns about their potential to create or widen healthcare disparities.61”
From TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods (2024) · cites this paper as [61]
While adequate representation of minoritised and underserved groups within datasets is one key element to achieving fairness goals, representation alone does not guarantee fairness.
“While adequate representation of minoritised and underserved groups within datasets is one key element to achieving fairness goals, representation alone does not guarantee fairness.”
From TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods (2024) · cites this paper as [61]
AI for healthcare has a propensity to cause individual or societal harm quickly at a large scale.
“its benefits are being increasingly overshadowed by its propensity to cause individual or societal harm quickly at a large scale [1,2].”
From Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers (2023) · cites this paper as [2]
Relatively few AI for healthcare initiatives are backed by empirical evidence.
“especially since relatively few initiatives are backed by empirical evidence [2].”
From Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers (2023) · cites this paper as [2]
Transparency issues include disclosure of an AI intervention's rate of false positive or false negative results (including for specific groups), changes to model performance over time, and the presence of bias.
“This includes the rate of false positive or false negative results (including for specific groups) [2], changes to model performance over time [31], and the presence of bias [41].”
From Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers (2023) · cites this paper as [2]
If people of different gender, ethnic and other sociodemographic backgrounds are not represented in the research, design and development of AI, these interventions could implicitly ignore the needs of these groups.
“If people of different gender, ethnic and other sociodemographic backgrounds are not represented in the research, design and development of AI, these interventions could implicitly ignore the needs of these groups [2].”
From Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers (2023) · cites this paper as [2]
Training AI on imbalanced datasets can fuel inequalities.
“Since training AI on imbalanced datasets data can fuel inequalities”
From Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers (2023) · cites this paper as [2]
Such checklists are an expressed priority when ML practitioners are asked directly about their needs.
“In any case, such checklists are an expressed priority when ML practitioners are asked directly about their needs, and should therefore be co-designed with their close input [2,17,21,192].”
From Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers (2023) · cites this paper as [2]
Black-box methodology provides local explanations of a model for a prediction.
“The blackbox methodology provides local explanations of a model for a prediction [41].”
From Explainable, trustworthy, and ethical machine learning for healthcare: A survey (2022) · cites this paper as [41]
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“At the same time, memory also raises significant concerns regarding privacy, data management, and bias [17].”
- Authors
- Chen, I. Y., Pierson, E., Rose, S., Joshi, S., Ferryman, K., & Ghassemi, M.
- Affiliations
- Massachusetts Institute of Technology (US)
- Microsoft (United States) (US)
- Stanford University (US)
- Vector Institute (CA)
- New York University (US)
- Venue
- Annual Review of Biomedical Data Science
- Publisher
- Annual Reviews
- Published
- 2021-07-20
- Volume
- 4
- Issue
- 1
- Pages
- 123-144
- ISSN
- 2574-3414 (ISSN-L)
- PMC
- PMC8362902
- OpenAlex
- W3087893815
- Cited by
- 645
- References
- 92
- Citations by year
- 2026: 169 · 2025: 149 · 2024: 128 · 2023: 91 · 2022: 74 · 2021: 28 · 2020: 6
- FWCI
- 14.21
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Annual Review of Biomedical Data Science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Funders
- National Institute on Minority Health and Health Disparities (DP2MD012722)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Keywords
- health care, health disparities, social justice, algorithmic fairness, problem selection, algorithmic bias
- MeSH terms
- Machine Learning, Delivery of Health Care, Health Facilities, Morals, Social Justice
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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