Researchers analyzed registry data covering the entire adult population of Sweden to explore new ways of identifying melanoma risk. The dataset included information such as age, sex, medical diagnoses, medication use, and socioeconomic status. In total, 6,036,186 individuals were included, and 38,582 (0.64%) developed melanoma over the five-year study period.
Much of the analysis was conducted by Martin Gillstedt:
“Our study shows that data which is already available within healthcare systems can be used to identify individuals at higher risk of melanoma,” says Martin Gillstedt, a doctoral student at the University of Gothenburg’s Sahlgrenska Academy and a statistician at Sahlgrenska University Hospital’s Department of Dermatology and Venereology. “This is not a form of decision support that is currently available in routine healthcare, but our results give a clear signal that registry data can be used more strategically in the future.”
AI Models Improve Melanoma Risk Prediction Accuracy
The researchers evaluated several artificial intelligence models and found clear differences in performance. The most advanced model correctly distinguished between people who later developed melanoma and those who did not in about 73% of cases. In comparison, using only age and sex resulted in an accuracy of around 64%.
By incorporating a broader range of factors such as diagnoses, medications, and sociodemographic information, the models were able to pinpoint smaller groups of individuals at significantly higher risk. Within these groups, the likelihood of developing melanoma within five years reached approximately 33%.
Targeted Screening Could Improve Detection and Efficiency
The study was led by Sam Polesie, Associate Professor of Dermatology and Venereology at the University of Gothenburg and a dermatologist at Sahlgrenska University Hospital:
“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. This would involve bringing population data into precision medicine and supplementing clinical assessments.”
Toward Personalized Melanoma Screening Strategies
While the findings are promising, the researchers note that additional studies and policy decisions are required before this approach can be used in routine healthcare. Still, the results highlight the potential of AI trained on large-scale registry data to support more personalized risk assessments and guide future melanoma screening strategies.
The research was conducted through a collaboration between the University of Gothenburg and Chalmers University of Technology.