Demographic Bias of Expert-Level Vision-Language Foundation Models in Medical Imaging

Yuzhe Yang, Yujia Liu, Xin Liu, Avanti Gulhane, Domenico Mastrodicasa, Wei Wu, Edward J Wang, Dushyant Sahani, Shwetak Patel

Keywords: Foundation model, Medical imaging, Healthcare, Bias, Fairness, Equity

Abstract: Advances in artificial intelligence (AI) have achieved expert-level performance in medical imaging applications. Notably, self-supervised vision-language foundation models can detect a broad spectrum of pathologies without relying on explicit training annotations. However, it is crucial to ensure that these AI models do not mirror or amplify human biases, disadvantaging historically marginalized groups such as females or Black patients. In this study, we investigate the algorithmic fairness of state-of-the-art vision-language foundation models in chest X-ray diagnosis across five globally-sourced datasets. Our findings reveal that compared to board-certified radiologists, these foundation models consistently underdiagnose marginalized groups, with even higher rates seen in intersectional subgroups such as Black female patients. Such biases present over a wide range of pathologies and demographic attributes. Further analysis of the model embedding uncovers its significant encoding of demographic information beyond human levels. Deploying medical AI systems with biases can intensify pre-existing care disparities, posing potential challenges to equitable healthcare access and raising ethical questions about their clinical applications. Code is available at: https://github.com/YyzHarry/vlm-fairness.

Submission Number: 7