Contrastive learning enhances fairness in pathology artificial intelligence systems.

ABSTRACT: 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 indepen

SUBMITTER: Lin SY 

PROVIDER: S-EPMC12765949 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature