A facial foundation model for multi-system biomarker and disease risk prediction with real-world mobile deployment

While most biomarkers currently rely on invasive laboratory testing, which limits large-scale or repeated screening, scalable non-invasive methods could transform population screening, early disease detection and personalized health management. Facial photographs, as a ubiquitous and non-invasive data source, offer such potential but remain underexplored for clinically relevant biomarker and disease risk prediction. Here, we present FaceFound, a facial foundation model trained over 10 million images through a progressive general-to-clinical pretraining strategy and evaluated across 62 biomarkers spanning eight physiological systems, many of which are well-established indicators of cardiometabolic, renal, and systemic diseases.. FaceFound consistently outperformed baseline architectures in biomarker prediction, achieving state-of-the-art performance for 45 (73%) biomarkers and demonstrating robust results across internal and four independent external cohorts (N = 206-2,247). Notably, FaceFound displayed superior performance to genetic models for the prediction of 14 out of 26 biomarkers with genetic scores available in the PGS catalog, highlighting its complementary value for disease risk assessment beyond inherited genetic susceptibility. Moreover, Face-predicted cardiovascular biomarkers demonstrated strong associations with coronary stenosis, enabling accurate prediction of cardiovascular disease risk and outperforming models based on laboratory-measured biomarkers. FaceFound further exhibited label efficiency, retaining predictive power with as few as 400 training samples, underscoring its value in low-resource settings. Moreover, FaceFound was deployed as a smartphone application, enabling real-time biomarker estimation and individualized disease risk reporting from a single self-captured facial photograph. These findings provide that FaceFound can reproducibly predict multi-system biomarkers and clinically relevant disease risk from facial images with real-world feasibility, establishing a paradigm for population-wide digital screening, early disease risk stratification and personalized risk assessment.