A Facial Foundation Model for Clinical Biomarker Prediction and Real‐World Mobile Deployment

ABSTRACT 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.