mlsanityData to 14 Oct 2026

Algorithm Fairness in AI for Medicine and Healthcare

arXiv 2110.00603 ↗First posted 1 Oct 2021cs.CV cs.LGcited by 21

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Abstract (arXiv)

In the current development and deployment of many artificial intelligence (AI) systems in healthcare, algorithm fairness is a challenging problem in delivering equitable care. Recent evaluation of AI models stratified across race sub-populations have revealed inequalities in how patients are diagnosed, given treatments, and billed for healthcare costs. In this perspective article, we summarize the intersectional field of fairness in machine learning through the context of current issues in healthcare, outline how algorithmic biases (e.g. - image acquisition, genetic variation, intra-observer labeling variability) arise in current clinical workflows and their resulting healthcare disparities. Lastly, we also review emerging technology for mitigating bias via federated learning, disentanglement, and model explainability, and their role in AI-SaMD development.

Key facts. Cited by 21 papers in the map (data through 14 Oct 2026). Builds on 125 of its 325 references that are in the map. First posted on arXiv 1 Oct 2021.

Builds on

125 of 325 references.

References · 125

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 11; 2023: 7; 2024: 2; 2025: 0; 2026: 1.

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  1. 2022MEDFAIR: Benchmarking Fairness for Medical Imagingcited by 68
  2. 2022Guidelines and Evaluation of Clinical Explainable AI in Medical Image Analysiscited by 35
  3. 2023Fair Machine Unlearning: Data Removal while Mitigating Disparitiescited by 26
  4. 2022A Review of Causality for Learning Algorithms in Medical Image Analysiscited by 18
  5. 2023Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methodscited by 17
  6. 2023A Review of the Role of Causality in Developing Trustworthy AI Systemscited by 14
  7. 2022Mitigating Health Disparities in EHR via Deconfoundercited by 7
  8. 2023Fairness Evaluation in Text Classification: Machine Learning Practitioner Perspectives of Individual and Group Fairnesscited by 7
  9. 2022Three Applications of Conformal Prediction for Rating Breast Density in Mammographycited by 6
  10. 2022Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairnesscited by 6
  11. 2022Enhancing Fairness of Visual Attribute Predictorscited by 3
  12. 2022A Survey on Preserving Fairness Guarantees in Changing Environmentscited by 2
9 more
  1. 2024Out-of-distribution Reject Option Method for Dataset Shift Problem in Early Disease Onset Predictioncited by 1
  2. 2024Ethical considerations of use of hold-out sets in clinical prediction model managementcited by 1
  3. 2022Fair Machine Learning in Healthcare: A Reviewcited by 0
  4. 2022Identifying and Combating Bias in Segmentation Networks by leveraging multiple resolutionscited by 0
  5. 2022Ex-Ante Assessment of Discrimination in Datasetcited by 0
  6. 2023Echoes of Biases: How Stigmatizing Language Affects AI Performancecited by 0
  7. 2023Learning Unbiased Image Segmentation: A Case Study with Plain Knee Radiographscited by 0
  8. 2023Beyond Fairness: Age-Harmless Parkinson's Detection via Voicecited by 0
  9. 2026Causal Transfer in Medical Image Analysiscited by 0