AI Facial Scans Help Doctors Assess Age and Survival Risk
Researchers at Mass General Brigham have developed an AI model called FAHR-Face that analyzes facial photographs to estimate biological age and predict survival outcomes in cancer patients. The tool aims to provide a scalable, quantitative method to assist physicians in making personalized treatment decisions.
Why it matters
This technology could revolutionize clinical decision-making by offering non-invasive, objective health assessments that help doctors determine a patient's physical resilience before intensive medical procedures.
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.
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