AI Tool Can Predict Cancer Survival From a Single Selfie, Study Suggests
Key Takeaways:
- Researchers at Mass General Brigham have developed an AI system, trained on more than 40 million facial photographs, that can estimate biological age and predict survival in people with cancer from an ordinary photograph of their face.
- Among patients with cancer treated with radiotherapy in the US, those whom the AI judged to look five or more years older than their actual age had a 21% higher risk of dying during an average four-year follow-up, while those who looked five or more years younger had a 17% lower risk.
- The researchers say the technology is intended to support, not replace, clinical judgement, and caution that more research is needed before it can be used widely.
A new approach to assessing patient health
Doctors have developed an artificial intelligence (AI) tool capable of predicting the survival chances of people with cancer from a single photograph of their face.
The system was custom-built by experts at Mass General Brigham, a not-for-profit, integrated academic healthcare system in Massachusetts, to estimate biological age and directly predict survival outcomes in patients diagnosed with cancer.
Trained on data from more than 40 million facial photographs spanning a range of ethnicities and ages, the system represents one of the most comprehensive demonstrations to date of how AI analysis of a selfie could help assess a person’s health and potentially guide their medical care.
The findings were published in the journal Lancet Digital Health.
Estimating biological age from a photograph
According to the results of the study examining the model, the system can estimate a person’s biological age – the age a person looks, rather than their actual, chronological age – and predict survival chances for people with cancer from an everyday photograph of their face.
Using the technology, the researchers developed two complementary tools:
- FAHR-FaceAge estimates a patient’s biological age and predicts cancer survival.
- FAHR-FaceSurvival predicts survival directly from a facial photograph, rather than inferring it through biological age.
Although both tools were derived from the same model, they captured different and complementary information about a patient’s health.
Different facial regions, different predictions
Each algorithm relied on distinct areas of the face to make its predictions of age and mortality risk.
FAHR-FaceAge focuses on the creases running from the sides of the nose down to the corners of the mouth, the forehead area just above the eye sockets, and the temples. FAHR-FaceSurvival, by contrast, evaluates the region beneath the eyes and around the bridge of the nose.
“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,” said Hugo Aerts, a co-senior author of the study. “To our knowledge, this is the first demonstration that an AI model can directly predict cancer survival from a simple photograph.”
Looking older linked to higher risk of death
Among patients with cancer treated with radiotherapy in the US, those whom FAHR-FaceAge assessed as looking five or more years older than their actual age had a 21% higher risk of dying during the average four-year follow-up period compared with the rest of the group. Patients who looked five or more years younger had a 17% lower risk.
“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 Aerts. “This foundation-model approach gives us a scalable way to develop multiple facial health biomarkers rather than training each model from scratch.”
The study’s results suggest that a person’s biological age estimate is closely linked to their physical health, which may reflect their ability to withstand certain treatments.
Supporting difficult treatment decisions
The researchers believe the breakthrough could pave the way for AI-based facial analysis to help doctors make difficult decisions about the most appropriate course of treatment for their patients. The tool could also improve predictions of the risk of death or complications following major surgery or other cancer treatments.
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.”
As AI tools such as this move closer to clinical practice, healthcare professionals will increasingly need the skills to understand and critically evaluate them. The College of Contemporary Health’s AI Essentials for Primary Care course is designed to help clinicians build confidence in navigating the growing role of AI in patient care.
Lifestyle factors and facial ageing
The researchers also found that lifestyle factors were associated with facial ageing. Heavy alcohol use, drug use and smoking, for example, were linked to people looking biologically older in the AI’s analysis of their facial photographs.
Consistent across real-world photo variations
“Another surprising finding was how consistent FAHR-FaceAge was across common real-world photo variations, including pose, lighting, facial expression and makeup,” said Mak. “It was also stable across most tested plastic surgery procedures, with only a small shift observed after rhinoplasty.”
More research needed
While a single facial photograph could offer a simple, low-cost way to support personalised treatment decisions, the doctors cautioned that further research is required before the tool can be adopted widely.
CCH insight
AI is rapidly reshaping how patient health is assessed, from facial analysis tools like FAHR-Face to a growing range of digital technologies entering everyday care. Understanding how these tools work, where their limitations lie and how they can complement clinical judgement is becoming essential for healthcare professionals. Our AI Essentials for Primary Care CPD course helps you build the knowledge and confidence to engage with AI safely and effectively in practice.
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Source: Lancet Digital Health
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