Doctors’ Snap Judgments May Soon Get an AI Upgrade
Washington: Doctors often start exams with the "eyeball test" — a quick assessment of whether a patient appears older or younger than their age. This intuitive judgment could soon be enhanced by artificial intelligence.
FaceAge: A Deep Learning Algorithm
FaceAge, a deep learning algorithm described in The Lancet Digital Health, converts a simple headshot into a number that more accurately reflects a person’s biological age rather than their age on their chart.
Cancer Patients May Be 5 Years Older Biologically
Trained on tens of thousands of photographs, FaceAge pegged cancer patients on average as biologically five years older than healthy peers. The study’s authors say it could help doctors decide who can safely tolerate harsh treatments and who might fare better with gentler approaches.
Hypothetical Patients
Consider two hypothetical patients: a spry 75-year-old whose biological age is 65, and a frail 60-year-old whose biology reads 70. Aggressive radiation might be appropriate for the former but risky for the latter.
Growing Evidence of Different Aging Rates
Growing evidence shows humans age at different rates, influenced by genes, stress, exercise, and habits like smoking or drinking. While pricey genetic tests can reveal how DNA wears over time, FaceAge promises insight using only a selfie.
Testing and Validation
The model was trained on 58,851 portraits of presumed-healthy adults over 60, culled from public datasets. It was then tested on 6,196 cancer patients treated in the United States and the Netherlands, using photos snapped just before radiotherapy. Patients with malignancies looked on average 4.79 years older biologically than their chronological age.
Predictive Accuracy
Among cancer patients, a higher FaceAge score strongly predicted worse survival, even after accounting for actual age, sex, and tumor type. The hazard rose steeply for anyone whose biological reading tipped past 85.
Unique Insights
FaceAge appears to weigh the signs of aging differently than humans do. For example, being gray-haired or balding matters less than subtle changes in facial muscle tone.
AI’s Potential and Challenges
AI tools have faced scrutiny for under-serving non-white people. Mak said preliminary checks revealed no significant racial bias in FaceAge’s predictions, but the group is training a second-generation model on 20,000 patients. They’re also probing how factors like makeup, cosmetic surgery, or room lighting variations could fool the system.
Ethical Considerations
Ethics debates loom large. An AI that can read biological age from a selfie could prove a boon for clinicians, but also tempting for life insurers or employers seeking to gauge risk.
Future Directions
The researchers are planning to open a public-facing FaceAge portal where people can upload their own pictures to enroll in a research study to further validate the algorithm. Commercial versions aimed at clinicians may follow, but only after more validation.
Published On May 9, 2025 at 11:32 AM IST
