Intersectional Fairness in Vision-Language Models for Medical Image Disease Classification
Published in arXiv preprint (under review), 2025
A cross-modal alignment consistency framework that equalises diagnostic confidence across intersectional subgroups, reducing fairness disparities by 20–35% across multiple medical imaging benchmarks.
Recommended citation: Yupeng Zhang, Adam G. Dunn, Usman Naseem, Jinman Kim. (2025). "Intersectional Fairness in Vision-Language Models for Medical Image Disease Classification." arXiv preprint arXiv:2512.15249. https://arxiv.org/abs/2512.15249