I have some doubts. Indeed, this capability was only tested on individuals with cancer. Cancer is an extremely deadly disease!
It appears more that this model is capable of better forecasting the remaining days until death of a cancer patient from a single selfie photo.
"An artificial-intelligence tool can estimate a person’s biological age and chance of dying from cancer by analysing a single photo of their face.
The model was trained using more than 40 million photos of faces across all age groups and was tested on two, separate groups of people with cancer.
Of the 122 people who were deemed to be at greatest risk of death from cancer by the AI, more than half died within 90 days.
“Clinicians already intuitively assess patient health based on appearance,” report the authors; AI tools “standardize and quantify this intuitive practice”. ..."
From the abstract:
"Summary
Background
Facial appearance offers a non-invasive window into health. We built Foundation Artificial Intelligence for Health Recognition-Face (FAHR-Face), a foundation model trained on more than 40 million facial photographs and fine-tuned it for two distinct tasks: biological age estimation (FAHR-FaceAge) and survival prediction (FAHR-FaceSurvival).
Methods
FAHR-FaceAge underwent two-stage, age-balanced fine-tuning on ten public image datasets;
FAHR-FaceSurvival was fine-tuned on 34 363 photographs of patients with cancer. Model robustness (cosmetic surgery, make-up, pose, and lighting) and independence (attention mapping) were tested. Both models were clinically tested in two independent cancer patient datasets with survival analysed by multivariate Cox models and adjusted for clinical prognostic factors.
Findings
For age estimation, FAHR-FaceAge had the lowest mean absolute error of 5·1 years on public datasets, outperforming benchmark models and maintaining accuracy across the full human lifespan.
In patients with cancer, FAHR-FaceAge outperformed a previous age estimation model in survival prognostication.
FAHR-FaceSurvival showed robust prediction of mortality, and the highest-risk quartile had more than triple the mortality of the lowest (adjusted hazard ratio 3·23; p<0·0001). These findings were validated in the independent cohort and both models showed generalisability across age, sex, race, and cancer subgroups.
The two algorithms provided distinct, complementary prognostic information; attention mapping revealed that each model relied on distinct facial regions. The combination of FAHR-FaceAge and FAHR-FaceSurvival improved prognostic accuracy.
Interpretation
A single foundation model can generate inexpensive, scalable facial biomarkers that capture both biological ageing and disease-related mortality risk. The foundation model enabled effective training by use of relatively small clinical datasets. Routine quantitative facial analysis could enhance risk stratification when traditional biomarkers are unavailable."
Can AI predict whether you’ll die from cancer from just an image of your face? "Researchers tasked an algorithm with predicting cancer patients’ risk of dying. For some people, it was shockingly accurate"
Figure 1 Study design and Harvard-RT patient dataset overview
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