false
AI facial health recognition tools help physicians assess age and survival risk to guide personalized treatment
Mass General Brigham researchers trained a new AI foundation model on more than 40 million facial images, and used it to develop AI tools to estimate biological age and directly predict survival risk in patients with cancer
Researchers are using AI tools to analyze simple facial photographs to determine key indicators of a person’s health. Mass General Brigham researchers built FAHR-Face (Foundation Artificial Intelligence for Health Recognition), a large AI foundation model trained on over 40 million face images. They used the model to develop two complementary tools: FAHR-FaceAge, which estimates a patient’s biological age with greater accuracy compared to prior age estimation algorithms, and FAHR-FaceSurvival, which directly predicts survival outcomes in patients with cancer from a face photograph. The model development and validation study showed that the deviation between a patient’s FAHR-FaceAge and their chronological age was independently linked to survival. It also demonstrated the robustness of the tools across common photo variations. The study is published in Lancet Digital Health.
“As the population ages, physicians need better ways to estimate physical reserve and frailty before recommending intensive treatments such as radiation, surgery or systemic therapy,” said corresponding author Raymond Mak, MD, a radiation oncologist at Mass General Brigham Cancer Institute and a faculty member in the Artificial Intelligence in Medicine (AIM) program at Mass General Brigham. “Our goal is to make face photograph-based health assessment more quantitative, consistent and scalable so it can support personalized treatment decisions rather than replace physician judgment.”
“The key technical advance is that FAHR-Face learns a general representation of the face from more than 40 million images, which can then be adapted to distinct health outcomes such as biological age and survival,” said Hugo Aerts, PhD, co-senior author and Director of AIM at Mass General Brigham. “This foundation-model approach gives us a scalable way to develop multiple facial health biomarkers rather than training each model from scratch.”
The image on the left depicts the features most important to FAHR-FaceSurvival’s predictions of cancer survival outcomes, while the image on the right shows the features most important to FAHR-FaceAge’s estimates of biological age. Areas shown in yellow had the greatest influence on each model’s predictions
Although FAHR-FaceAge and FAHR-FaceSurvival were derived from the same foundation model, they captured different and complementary information about a patient’s health. Their predictions were only weakly correlated, and both remained independently associated with survival when analyzed together. Combining the two measures further improved survival-model fit in an external validation cohort of cancer patients. Each algorithm also relied on distinct parts of the face to make the respective predictions of age and mortality risk. FAHR-FaceAge focuses on the nasolabial folds, the forehead area just above the orbits and the temples, while FAHR-FaceSurvival evaluates the region below the eyes and around the nasal bridge.
“FAHR-FaceSurvival advances the field of facial health recognition AI a step further by learning survival risk directly from facial photographs of patients with cancer,” Aerts said. “To our knowledge, this is the first demonstration that an AI model can directly predict cancer survival from a simple photograph.”
The researchers also found that lifestyle factors influence facial aging. For example, heavy alcohol use, drug use and smoking were associated with looking biologically older. Smoking showed the strongest pattern, with current smokers looking older than former smokers and former smokers looking older than people who had never smoked
“Another surprising finding was how consistent FAHR-FaceAge was across common real-world photo variations, including pose, lighting, facial expression and makeup,” noted Mak. “It was also stable across most tested plastic surgery procedures, with only a small shift observed after rhinoplasty.”
The team is also expanding this research through the FaceAge Research Study, an online, institutional review board-approved study open to the public at faceage.bwh.harvard.edu. Participants can upload a facial photograph and receive their own FaceAge research readout, while also contributing data that will help researchers improve and validate AI-based measures of facial aging and health across larger and more diverse populations.
Authorship: In addition to Mak, Mass General Brigham authors include Fridolin Haugg, Grace Lee, John He, Leonard Nürnberg, Dennis Bontempi, Danielle S. Bitterman, Vasco Prudente, Dmitrii Glubokov, Andrew Warrington, Suraj Pai, Christian Guthier, Benjamin H. Kann, Vadim N. Gladyshev, and Hugo JWL Aerts. Additional authors include Paul Catalano and Dirk De Ruysscher
Disclosures: Mak reports being on an Advisory Board for ViewRay and AstraZeneca; Consulting for AstraZeneca, Varian Medical Systems, and Sio Capital Management; Honorarium from Novartis and Springer Nature; and Research Funding from the National Institute of Health, ViewRay, AstraZeneca, Siemens Medical Solutions USA, Inc., and Varian Medical Systems. Bitterman reports Editorial, unrelated to this work: Associate Editor of Radiation Oncology of HemOnc.org, Associate Editor, Annals of Oncology, and Associate Editor, JCO Clinical Cancer Informatics; Research funding, unrelated to this work: American Cancer Society/American Society for Radiation Oncology (ASTRO-CSDG-24-1244514-01-CTPS) and National Institutes of Health/National Cancer Institute (R01CA294033); Advisory and consulting, unrelated to this work: MercuriaAI. Aerts reports advising and consulting for Onc.AI, Love Health, Sphera, Editas, AstraZeneca, and Bristol Myers Squibb, unrelated to this work. Kann reports research funding from the Botha-Chan Low Grade Glioma Consortium (National Institutes of Health [NIH]-USA K08DE030216-01). Haugg, Mak, and Aerts report that Mass General Brigham has filed a US provisional patent application on next-generation facial health algorithms: US Provisional Application No. 63/769,840. All other authors declare no competing interests. Additional author disclosures can be found in the paper.
Funding: This study was funded by the EU European Research Council
Paper cited: Haugg F et al. “Foundation Artificial Intelligence Models for Health Recognition Using Face Photographs (FAHR-Face): a model development and validation study” Lancet Digital Health DOI: 10.1016/j.landig.2026.101056
Media contact
Ryan JaslowProgram Director, External Communicationsrjaslow@mgb.org
About Mass General Brigham
Mass General Brigham is an integrated academic health care system, uniting great minds to solve the hardest problems in medicine for our communities and the world. Mass General Brigham connects a full continuum of care across a system of academic medical centers, community and specialty hospitals, a health insurance plan, physician networks, community health centers, home care, and long-term care services. Mass General Brigham is a nonprofit organization committed to patient care, research, teaching, and service to the community. In addition, Mass General Brigham is one of the nation’s leading biomedical research organizations with several Harvard Medical School teaching hospitals. For more information, please visit massgeneralbrigham.org.
