How AI identifies melanoma risk before diagnosis: Here’s everything to know

According to a recent study, a significant shift has been observed toward precision medicine, where AI is used not just to diagnose a disease someone already has, but to predict who is most likely to get it in the future. Researchers from the University of Gothenburg analyzed health data from approximately 6 million Swedish adults. Unlike traditional screening that looks primarily at age and gender, this AI model was fed big data including previous medical diagnoses, detailed medication histories and sociodemographic data.

The most advanced model correctly identified future melanoma patients in 73% of cases. For comparison, models using only age and gender were only 64% accurate. Researchers were able to identify very small groups where the risk of developing melanoma within five years was as high as 33%. Because melanoma can spread rapidly once it moves past the skin, early detection is the most effective way to lower the mortality rate.

“Our study shows that data which is already available within healthcare systems can be used to identify individuals at higher risk of melanoma,” said Martin Gillstedt, a doctoral student at the University of Gothenburg.

Because universal screening is both expensive and time-consuming, healthcare systems could use this AI to send targeted screening invitations to the highest-risk individuals. While the results are promising, the researchers noted that policy changes and further clinical validation are required before this AI risk score becomes a standard part of your medical record.

“Our analyses suggest that selective screening of small, high-risk groups could lead to both more accurate monitoring and more efficient use of healthcare resources,” lead author of the study Sam Polesie said.

The study demonstrated that AI models trained on large amounts of registry data could become pivotal for more risk-tailored screening and future strategies. Nonetheless, researchers underscore that further research and policy decisions are necessary before the method can be introduced into routine healthcare.