ArticleAnnual review of biomedical data science2021
Ethical Machine Learning in Healthcare.
Article in Annual review of biomedical data science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 251 papers, 7 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
251 citing papers in PubMed, 7 syntheses or guidelines pooled it.
- Opportunities and Challenges of Using Artificial Intelligence in Predicting Clinical Outcomes and Length of Stay in Neonatal Intensive Care Units: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Machine learning for myocarditis diagnosis using cardiovascular magnetic resonance: a systematic review, diagnostic test accuracy meta-analysis, and comparison with human physicians.The international journal of cardiovascular imaging · 2025Pooled it
- TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods: a Korean translation.Ewha medical journal · 2025Guideline
- Quantifying Sample Representation in Global Pharmacogenomic Studies of Major Depressive Disorder: A Systematic Review.Clinical and translational science · 2025Pooled it
- Examining inclusivity: the use of AI and diverse populations in health and social care: a systematic review.BMC medical informatics and decision making · 2025Pooled it
- Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations.The Lancet. Digital health · 2025Pooled it
- Application of machine learning for lung cancer survival prognostication-A systematic review and meta-analysis.Frontiers in artificial intelligence · 2024Pooled it
- Predicting intradialytic exercise intolerance in maintenance hemodialysis patients: an interpretable machine learning approach integrating functional assessments.Renal failure · 2026Observational
- Mitigating Domain Shift and Dataset Bias in Deep Learning Models for Cancer Diagnostics.Journal of imaging informatics in medicine · 2026Review
- AI and the Reproduction of Health Inequity: Redistribution-Translation-Accumulation Framework.Journal of medical Internet research · 2026Article
- Algorithmic fairness reporting in artificial intelligence for spinal deformity: a systematic review.Spine deformity · 2026Review
- Demographic subgroup-aware analysis of blood pressure predictive models.PLOS digital health · 2026Article
- Addressing Health Disparities through Community Engagement in Artificial Intelligence-Driven Prevention Science.Prevention science : the official journal of the Society for Prevention Research · 2026Review
- Large Language Models for Clinical Data Extraction in Workplace Injury Rehabilitation: Protocol for a Retrospective Pilot Study of Accuracy, Fairness, and Methodological Considerations in Workers' Compensation Medical Chart Review.JMIR research protocols · 2026Article
- Cell-based therapies of autoimmune diseases in the context of artificial intelligence development.Clinical and experimental medicine · 2026Review
- Applying Large Language Models in Perioperative Medicine With an Equity-Informed Perspective.Anesthesia and analgesia · 2026Article
- PREFER-IT: A transdisciplinary co-created framework to realise inclusive health AI.PLOS digital health · 2026Article
- Pre-Deployment Audit of Actionability and Equity in Severe Tooth Loss Prediction Using Constrained Algorithmic Recourse and Temporal Validation.Bioengineering (Basel, Switzerland) · 2026Article
- Education Research: Validation of an AI-Powered Educational Platform for Neuroanatomical Localization and Stroke Syndrome Recognition: A Multicenter Study.Neurology. Education · 2026Article
- Foundation models in medical image analysis: A systematic review and quantitative analysis.Medical image analysis · 2026Review
191 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
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.