Experts at Mass General Brigham, a Massachusetts-based academic healthcare system, have created artificial intelligence technology capable of estimating biological age and predicting survival outcomes in cancer patients through analysis of facial photographs. The system represents one of the most comprehensive demonstrations to date of how AI can assess patient health from a selfie and potentially inform medical decision-making.
The research team developed two complementary tools derived from the same underlying model. FAHR-FaceAge estimates a patient’s biological age—how old a person appears rather than their chronological age—and predicts cancer survival. FAHR-FaceSurvival predicts survival directly from facial analysis without calculating biological age first. Although both tools originated from the same model, they extract different information and focus on distinct facial regions. FAHR-FaceAge emphasizes creases around the nose and mouth, forehead, and temples, while FAHR-FaceSurvival analyzes the area beneath the eyes and around the nasal bridge.
The study examined cancer patients who underwent radiotherapy in the United States. Results showed that patients whose appearance suggested they were five or more years older than their actual age faced a 21% increased mortality risk during an average four-year follow-up period, compared with other patients. Conversely, those who appeared five or more years younger experienced a 17% lower risk. The findings indicate that biological age estimates correlate closely with physical health and treatment tolerance.
Researchers trained the AI system on data from more than 40 million facial photographs representing various ethnicities and age groups. The research was published in the Lancet Digital Health journal. Scientists noted that lifestyle factors including heavy alcohol consumption, drug use, and smoking were associated with appearing biologically older in the AI analysis. The system also demonstrated consistency across common photographic variations such as pose, lighting, facial expression, and makeup, with minimal changes observed following most plastic surgery procedures except rhinoplasty.
Medical professionals emphasized that while facial photograph analysis offers a convenient and cost-effective approach to support personalized treatment decisions, additional research is necessary before widespread clinical implementation. The technology could potentially assist physicians in assessing physical reserve and frailty before recommending intensive treatments such as radiation therapy, surgery, or systemic therapy.
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