Study Highlights Two AI-Powered Tools Analyzing Facial Photographs to Determine Person's Health

The tools estimate a patient's biological age and predict survival outcomes in patients with cancer from face photographs.

Matt MacKenzie

Oct. 26, 2026

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Key Highlights

FAHR-FaceAge estimated biological age more accurately than earlier age-estimation algorithms.
FAHR-FaceSurvival predicted cancer patients’ survival outcomes from facial photographs.
The tools’ measures independently related to survival, and combining them improved model accuracy.
Smoking, heavy alcohol use, and drug use were associated with older-looking faces; smoking showed the strongest pattern.

6adf9b87091ceaade06198c4 Artificial Intelligence

A study in Lancet Digital Health found that two complementary AI-powered tools that analyze facial photographs were able to help determine key indicators of a person's health.

The first tool, FAHR-FaceAge, estimates a patient's biological age with greater accuracy compared to prior age estimation algorithms, the study showed. FAHR-FaceSurvival, the other tool, directly predicts survival outcomes in patients with cancer from a face photograph.

One of the co-senior authors said 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."

The two tools' predictions were only weakly correlated, and both remained independently associated with survival. Combining the two measures improved the model's accuracy.

The researchers found that lifestyle factors influence facial aging. Heavy alcohol use, drug use, and smoking were associated with looking biologically older. Smoking showed the strongest pattern.