Shih‐Yen Lin, Pei-Chen Tsai, Fang-Yi Su, Chun-Yen Chen, Fei Li, Junhan Zhao, Yin Ying Ho, Michael T. Lee, Elizabeth Healey, Po-Jen Lin, Ting‐Wan Kao, Dmytro Vremenko, Thomas Roetzer-Pejrimovsky, Lynette M. Sholl, Deborah Dillon, Nancy U. Lin, David M. Meredith, Keith L. Ligon, Ying‐Chun Lo, Nipon Chaisuriya, David J. Cook, Adelheid Wöehrer, Jeffrey A. Meyerhardt, Shuji Ogino, MacLean P. Nasrallah, Jeffrey A. Golden, Sabina Signoretti, Jung-Hsien Chiang, Kun‐Hsing Yu
AI-enhanced pathology evaluation systems hold significant potential to improve cancer diagnosis but frequently exhibit biases against underrepresented populations due to limited diversity in training data. Here, we present the Fairness-aware Artificial Intelligence Review for Pathology (FAIR-Path), a framework that leverages contrastive learning and weakly supervised machine learning to mitigate bias in AI-based pathology evaluation. In a pan-cancer AI fairness analysis spanning 20 cancer types, we identify significant performance disparities in 29.3% of diagnostic tasks across demographic groups defined by self-reported race, gender, and age. FAIR-Path effectively mitigates 88.5% of these disparities, with external validation showing a 91.1% reduction in performance gaps across 15 independent cohorts. We find that variations in somatic mutation prevalence among populations contribute to these performance disparities. FAIR-Path represents a promising step toward addressing fairness challenges in AI-powered pathology diagnoses and provides a robust framework for mitigating bias in medical AI applications.