  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.
