Chest radiography (CXR) is widely accessible, and its ability to capture subtle manifestations of systemic disease remains underexplored.
We developed a contrastively pretrained multimodal CXR foundation model using large-scale image–report pairs and evaluated its capacity to predict diverse human diseases.
Using 1,074 phecodes derived from electronic health records, we trained linear probes on frozen image embeddings and validated performance across three independent cohorts (n = 90,911; n = 79,786; n = 60,282).
Importantly, the embeddings captured imaging signatures associated with near-term cardiovascular events and critical illness.
These findings demonstrate that CXR foundation model embeddings encode rich, clinically relevant information beyond conventional interpretation, providing a scalable and interpretable framework for comprehensive disease profiling and a foundation for future prospective validation.
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