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Unmasking bias in artificial intelligence: a systematic review of bias detection and mitigation strategies in electronic health record-based models.
April 17, 2024
Chen F, Wang L, Hong J, et al. J Am Med Inform Assoc. 2024;31(5):1172-1183.
When biased data are used for research, the results may reflect the same biases if appropriate precautions are not taken. In this systematic review, researchers describe possible types of bias (e.g., implicit, selection) that can result from research with artificial intelligence (AI) using electronic health record (EHR) data. Along with recommendations to reduce introducing bias into the data model, the authors stress the importance of standardized reporting of model development and real-world testing.
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