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Ethical Machine Learning in Health Care

arXiv 2009.10576 ↗First posted 22 Sep 2020cs.CY cs.AI cs.LGcited by 112

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Abstract (arXiv)

The use of machine learning (ML) in health care raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical considerations for equitable ML in the advancement of health care. Specifically, we frame ethics of ML in health care 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 post-deployment considerations. We close by summarizing recommendations to address these challenges.

Key facts. Cited by 112 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Sep 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 13; 2022: 26; 2023: 23; 2024: 13; 2025: 21; 2026: 16.

112 papers.

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  1. 2022Large Language Models Encode Clinical Knowledgecited by 1,807
  2. 2023Towards Expert-Level Medical Question Answering with Large Language Modelscited by 382
  3. 2023Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Datacited by 66
  4. 2022Improving the Fairness of Chest X-ray Classifierscited by 52
  5. 2022Diagnosing failures of fairness transfer across distribution shift in real-world medical settingscited by 45
  6. 2024A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Modelscited by 45
  7. 2022The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanationscited by 41
  8. 2023Diagnosing Model Performance Under Distribution Shiftcited by 37
  9. 2021What's in a Summary? Laying the Groundwork for Advances in Hospital-Course Summarizationcited by 35
  10. 2021RadFusion: Benchmarking Performance and Fairness for Multimodal Pulmonary Embolism Detection from CT and EHRcited by 32
  11. 2023Artificial General Intelligence for Medical Imaging Analysiscited by 32
  12. 2022Learning to Revise References for Faithful Summarizationcited by 28
38 more
  1. 2022Healthsheet: Development of a Transparency Artifact for Health Datasetscited by 23
  2. 2022OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semanticscited by 23
  3. 2022Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular datacited by 22
  4. 2021Algorithm Fairness in AI for Medicine and Healthcarecited by 21
  5. 2022Longitudinal Fairness with Censorshipcited by 21
  6. 2022Write It Like You See It: Detectable Differences in Clinical Notes By Race Lead To Differential Model Recommendationscited by 20
  7. 2025HiMAE: Hierarchical Masked Autoencoders Discover Resolution-Specific Structure in Wearable Time Seriescited by 19
  8. 2022Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcarecited by 16
  9. 2023Coarse race data conceals disparities in clinical risk score performancecited by 15
  10. 2023NLP for Maternal Healthcare: Perspectives and Guiding Principles in the Age of LLMscited by 14
  11. 2021Through the Data Management Lens: Experimental Analysis and Evaluation of Fair Classificationcited by 13
  12. 2021Medical Imaging and Machine Learningcited by 12
  13. 2021A comparison of approaches to improve worst-case predictive model performance over patient subpopulationscited by 12
  14. 2022A Sandbox Tool to Bias(Stress)-Test Fairness Algorithmscited by 12
  15. 2024Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcarecited by 12
  16. 2025U-Fair: Uncertainty-based Multimodal Multitask Learning for Fairer Depression Detectioncited by 12
  17. 2021Clustering Interval-Censored Time-Series for Disease Phenotypingcited by 11
  18. 2023Individual Fairness under Uncertaintycited by 11
  19. 2022Multi-disciplinary fairness considerations in machine learning for clinical trialscited by 8
  20. 2023Domain constraints improve risk prediction when outcome data is missingcited by 8
  21. 2025In the Picture: Medical Imaging Datasets, Artifacts, and their Living Reviewcited by 8
  22. 2026Position: LLMs Must Use Functor-Based and RAG-Driven Bias Mitigation for Fairnesscited by 8
  23. 2022Mitigating Health Disparities in EHR via Deconfoundercited by 7
  24. 2023Towards unraveling calibration biases in medical image analysiscited by 7
  25. 2021Pulling Up by the Causal Bootstraps: Causal Data Augmentation for Pre-training Debiasingcited by 6
  26. 2022When Personalization Harms: Reconsidering the Use of Group Attributes in Predictioncited by 6
  27. 2022Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairnesscited by 6
  28. 2023Optimizing fairness tradeoffs in machine learning with multiobjective meta-modelscited by 6
  29. 2024Understanding Disparities in Post Hoc Machine Learning Explanationcited by 6
  30. 2025FoMoH: A clinically meaningful foundation model evaluation for structured electronic health recordscited by 6
  31. 2025Understanding challenges to the interpretation of disaggregated evaluations of algorithmic fairnesscited by 6
  32. 2021ESCADA: Efficient Safety and Context Aware Dose Allocation for Precision Medicinecited by 5
  33. 2022Identifying the Context Shift between Test Benchmarks and Production Datacited by 5
  34. 2024The Case for Globalizing Fairness: A Mixed Methods Study on Colonialism, AI, and Health in Africacited by 5
  35. 2022Estimating Structural Disparities for Face Modelscited by 4
  36. 2022Trucks Don't Mean Trump: Diagnosing Human Error in Image Analysiscited by 4
  37. 2022Minimax Optimal Estimation of Stability Under Distribution Shiftcited by 4
  38. 2023FEDD -- Fair, Efficient, and Diverse Diffusion-based Lesion Segmentation and Malignancy Classificationcited by 4

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