mlsanityData to 14 Oct 2026

A survey of recent methods for addressing AI fairness and bias in biomedicine

arXiv 2402.08250 ↗First posted 13 Feb 2024cs.AIcited by 5

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Abstract (arXiv)

Artificial intelligence (AI) systems have the potential to revolutionize clinical practices, including improving diagnostic accuracy and surgical decision-making, while also reducing costs and manpower. However, it is important to recognize that these systems may perpetuate social inequities or demonstrate biases, such as those based on race or gender. Such biases can occur before, during, or after the development of AI models, making it critical to understand and address potential biases to enable the accurate and reliable application of AI models in clinical settings. To mitigate bias concerns during model development, we surveyed recent publications on different debiasing methods in the fields of biomedical natural language processing (NLP) or computer vision (CV). Then we discussed the methods that have been applied in the biomedical domain to address bias. We performed our literature search on PubMed, ACM digital library, and IEEE Xplore of relevant articles published between January 2018 and December 2023 using multiple combinations of keywords. We then filtered the result of 10,041 articles automatically with loose constraints, and manually inspected the abstracts of the remaining 890 articles to identify the 55 articles included in this review. Additional articles in the references are also included in this review. We discuss each method and compare its strengths and weaknesses. Finally, we review other potential methods from the general domain that could be applied to biomedicine to address bias and improve fairness.The bias of AIs in biomedicine can originate from multiple sources. Existing debiasing methods that focus on algorithms can be categorized into distributional or algorithmic.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 41 of its 94 references that are in the map. First posted on arXiv 13 Feb 2024.

Builds on

41 of 94 references.

References · 41

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29 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 2; 2026: 1.

5 papers.

  1. 2025Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessingcited by 5
  2. 2024Beyond Multiple-Choice Accuracy: Real-World Challenges of Implementing Large Language Models in Healthcarecited by 4
  3. 2024Entry-level guide to the use of large language models for medical researchcited by 3
  4. 2025A Gray Literature Study on Fairness Requirements in AI-enabled Software Engineeringcited by 0
  5. 2026Equity Bias: An Ethical Framework for AI Designcited by 0