A Facial Foundation Model for Clinical Biomarker Prediction and 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 and personalized health management. Facial photographs, as a ubiquitous and non-invasive data source, offer such potential but remain underexplored for clinical biomarker prediction. Existing supervised approaches are constrained by the availability of clinically labeled data, whereas general-purpose vision models are optimized for non-clinical tasks and may inadequately capture subtle, multiscale, and spatially distributed clinical facial features. To address these limitations, we developed MedicalFaceFound, a facial foundation model pretrained on over 10 million images and evaluated across 62 biomarkers spanning eight physiological systems. It performed best among the Swin-Large and ResNet18 for 45 (73%) biomarkers and generalized across four independent external cohorts (median Pearson's r = 0.172), with strong performance for RBC, eGFR, and HDL-C (median r = 0.478, 0.442, and 0.430, respectively). MedicalFaceFound also outperformed polygenic risk score models for 14 of 26 biomarkers, and face-estimated cardiovascular biomarkers were strongly associated with coronary stenosis (AUC = 0.66). The model retained predictive performance with 400 labeled samples and was feasible to deploy as a smartphone application, supporting scalable, non-invasive biomarker assessment and personalized risk screening.