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Algorithm Fairness in AI for Medicine and Healthcare

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arxiv 2110.00603 v2 pith:B2K7LGUM submitted 2021-10-01 cs.CV cs.LG

Richard J. Chen , Tiffany Y. Chen , Jana Lipkova , Judy J. Wang , Drew F.K. Williamson , Ming Y. Lu , Sharifa Sahai , Faisal Mahmood This is my paper · ORCID
classification cs.CVcs.LG
keywords healthcarecurrentfairnessalgorithmdevelopmentlearningacquisitionacross
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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.

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    cs.CV 2026-03 accept novelty 5.0 of 10

    Causal Transfer Learning unifies structural causal models, invariant risk minimisation and counterfactuals with transfer learning to produce domain-robust medical image models.

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