A proposed hormonal-dynamic validation protocol and Endocrine Volatility Score aim to address structural bottlenecks in clinical AI fairness for women across different hormonal states.
We identify the absence of hormonal-dynamic validation as a structural bottleneck in clinical AI fairness and propose a concrete protocol to address it. The protocol centers on a tiered multi-modal data stack anchored by nocturnal basal heart rate variability, serum hormone assays, and STRAW+10 menopausal staging, applied within a longitudinal mixed-effects framework that treats race as a moderator of hormonal transition slope. We introduce the Endocrine Volatility Score (EVS), the coefficient of variation of a model’s true positive rate across hormonal states as a quantitative transparency metric for a public Hormonal Fidelity Index.
Inna Rytsareva (2026) studied Clinical AI fairness in women. Hormonal-dynamic validation protocol was evaluated. A proposed hormonal-dynamic validation protocol and Endocrine Volatility Score aim to address structural bottlenecks in clinical AI fairness for women across different hormonal states.