“FAHR-FaceSurvival advances the field of facial health recognition AI"
Doctors have developed an artificial intelligence (AI) tool that can predict cancer patients’ chances of survival from a single photograph of their face.
Developed by experts at Mass General Brigham, a nonprofit integrated academic healthcare system in Massachusetts, the technology estimates biological age and predicts survival outcomes in cancer patients.
It was trained on data from more than 40 million facial photographs covering a range of ages and ethnicities.
The findings, published in The Lancet Digital Health, detail two complementary tools developed from the same AI model.
FAHR-FaceAge estimates biological age, or how old a person appears rather than their actual age, and uses this to predict cancer survival. FAHR-FaceSurvival predicts survival directly from a facial photograph without relying on biological age.
Although the tools use the same underlying model, they capture different information about a patient’s health and analyse distinct facial regions.
FAHR-FaceAge focuses on the creases running from the sides of the nose to the corners of the mouth, the forehead above the eye sockets and the temples. FAHR-FaceSurvival examines the area below the eyes and around the nasal bridge.
Hugo Aerts, a co-senior author of the study, said: “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.
“To our knowledge, this is the first demonstration that an AI model can directly predict cancer survival from a simple photograph.”
Researchers examined cancer patients who received radiotherapy in the United States and found that those who appeared at least five years older than their actual age had a 21% higher risk of dying during the average four-year follow-up than other patients.
Those who appeared at least five years younger had a 17% lower risk.
The results suggest biological age estimates derived from facial photographs could provide information about patients’ physical health and their ability to withstand treatment.
However, the findings establish an association between estimated biological age and survival risk rather than proving that facial ageing directly causes poorer outcomes.
Aerts added: “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.
“This foundation-model approach gives us a scalable way to develop multiple facial health biomarkers rather than training each model from scratch.”
The technology could eventually help doctors assess the risks of major surgery, radiotherapy and other cancer treatments, supporting decisions about the most appropriate approach for individual patients.
Dr Raymond Mak, a radiation oncologist at Mass General Brigham, said:
“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.
“Our goal is to make face photograph-based health assessment more quantitative, consistent and scalable so it can support personalised treatment decisions rather than replace physician judgment.”
Researchers also found that lifestyle factors were associated with facial ageing. Heavy alcohol use, drug use and smoking were linked to people appearing biologically older in the AI analysis.
FAHR-FaceAge also produced consistent estimates despite variations in photographs, including differences in pose, lighting, facial expression and makeup.
Its predictions remained stable across most of the plastic surgery procedures tested, although researchers observed a small shift following rhinoplasty.
Dr Mak said: “Another surprising finding was how consistent FAHR-FaceAge was across common real-world photo variations, including pose, lighting, facial expression and makeup.
“It was also stable across most tested plastic surgery procedures, with only a small shift observed after rhinoplasty.”
The researchers stated that further studies are needed before the technology can be widely used in clinical practice.
While analysing a single facial photograph could offer a relatively simple, low-cost way to support personalised treatment decisions, the tool is intended to assist rather than replace doctors’ clinical judgement.
Further research will be needed to assess its reliability across different patient populations and medical settings and determine whether its use can improve patient care.
