FAST-CAD: A Fairness-Aware Framework for Non-Contact Stroke Diagnosis

TL;DR

FAST-CAD introduces a fairness-aware framework combining domain-adversarial training and group distributionally robust optimization for equitable non-contact stroke diagnosis, ensuring accurate results across diverse demographic groups.

Contribution

It presents a novel, theoretically grounded approach that integrates DAT and Group-DRO, with convergence guarantees and fairness bounds, for fair medical AI diagnostics.

Findings

Achieves superior diagnostic accuracy across demographic groups.

Maintains fairness and robustness in stroke diagnosis.

Provides theoretical guarantees for convergence and fairness bounds.

Abstract

Stroke is an acute cerebrovascular disease, and timely diagnosis significantly improves patient survival. However, existing automated diagnosis methods suffer from fairness issues across demographic groups, potentially exacerbating healthcare disparities. In this work we propose FAST-CAD, a theoretically grounded framework that combines domain-adversarial training (DAT) with group distributionally robust optimization (Group-DRO) for fair and accurate non-contact stroke diagnosis. Our approach is built on domain adaptation and minimax fairness theory and provides convergence guarantees and fairness bounds. We curate a multimodal dataset covering 12 demographic subgroups defined by age, gender, and posture. FAST-CAD employs self-supervised encoders with adversarial domain discrimination to learn demographic-invariant representations, while Group-DRO optimizes worst-group risk to ensure robust performance across all subgroups. Extensive experiments show that our method achieves superior diagnostic performance while maintaining fairness across demographic groups, and our theoretical analysis supports the effectiveness of the unified DAT + Group-DRO framework. This work provides both practical advances and theoretical insights for fair medical AI systems.