March 23, 2024Open Access

Unmasking bias in artificial intelligence: a systematic review of bias detection and mitigation strategies in electronic health record-based models

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Abstract

This review highlights evolving strategies to mitigate bias in EHR-based AI models, emphasizing the urgent need for both standardized and detailed reporting of the methodologies and systematic real-world testing and evaluation. Such measures are essential for gauging models' practical impact and fostering ethical AI that ensures fairness and equity in healthcare.

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Chen et al. (Sat,) studied this question.

synapsesocial.com/papers/69d6987e9b6ed6ae1a029e12 https://doi.org/https://doi.org/10.1093/jamia/ocae060

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