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.

翻译：人工智能（AI）系统有望彻底改变临床实践，包括提升诊断准确性和手术决策水平，同时降低成本和人力投入。然而，必须认识到这些系统可能延续社会不平等或表现出基于种族、性别等因素的偏见。此类偏见可能出现在AI模型开发之前、期间或之后，因此理解并应对潜在偏见对于确保AI模型在临床环境中的准确可靠应用至关重要。为缓解模型开发过程中的偏见问题，我们系统调研了生物医学自然语言处理（NLP）或计算机视觉（CV）领域近期关于不同去偏方法的文献，并讨论了已在生物医学领域应用的去偏策略。我们在PubMed、ACM数字图书馆和IEEE Xplore数据库中，使用多组关键词组合检索了2018年1月至2023年12月间发表的相关文献。经宽松约束条件自动筛选后，我们对剩余890篇文献的摘要进行人工审查，最终纳入55篇综述文献，同时补充了参考文献中的相关研究。本文详细分析各方法并比较其优劣，最后探讨了通用领域中可迁移至生物医学以消除偏见、提升公平性的潜在方案。生物医学AI的偏见来源具有多样性，现有基于算法的去偏方法可分为分布型与算法型两类。