Hidden in Plain Sight: Vector Embeddings give away Demographic Information

Foundation models are widely used for compressing complex data into vector embeddings (vembs), offering reduced storage and computational efficiency, and significant improvements in diagnostic accuracy, automation, and efficiency in medical imaging. However, concerns remain that these vembs may encode demographic features, which could provide a pathway for bias in AI models in medical imaging. This study investigates whether demographic attributes – including sex, age, ethnicity, and insurance type – are embedded in vembs derived from chest radiographs in the MIMIC-CXR and CheXpert datasets. We generate vembs using three different state-of-the-art contrastive learning-based foundation models, namely, CXR Foundation, MedCLIP, and BiomedCLIP, assessing demographic predictability. Through rigorous statistical analysis and machine learning evaluations, we demonstrate substantial demographic encoding, indicating a plausible pathway through which bias may propagate. Our findings provide cautionary evidence supporting the need for further investigation and auditing of potential biases in vemb-based medical imaging predictions.