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.

人工智能系统在临床实践中有潜力革新，包括提高诊断准确性和手术决策，并降低成本和人力。然而，需要认识到这些系统可能会延续社会不公平或表现出以种族或性别为基础的偏见。为了准确可靠地应用人工智能模型于临床环境中，理解和解决潜在的偏见至关重要。通过调查生物医学自然语言处理或计算机视觉领域的最新出版物，我们对生物医学领域中应用的偏见处理方法进行了概述，并讨论了每种方法的优势和弱点。最后，我们还回顾了从一般领域中可能应用于生物医学领域以解决偏见和提高公平性的其他潜在方法。