  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, thereby
disadvantaging historically marginalized groups such as females or Black
patients. The manifestation of such biases could systematically delay essential
medical care for certain patient subgroups. 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 demographic
biases present over a wide range of pathologies and demographic attributes.
Further analysis of the model embedding uncovers its significant encoding of
demographic information. Deploying AI systems with these biases in medical
imaging can intensify pre-existing care disparities, posing potential
challenges to equitable healthcare access and raising ethical questions about
their clinical application.
