Artificial intelligence (AI) can identify early risk pattern among individuals at higher risk of melanoma, a new study showed on Wednesday.
The study was based on registry data that is routinely collected on the whole of Sweden’s adult population.
The analysed data included age, sex, diagnoses, use of medications and socioeconomic status.
Of the 6,036,186 individuals included, 38,582 (0.64 per cent) developed melanoma during the five years of the study.
“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’s Sahlgrenska Academy.
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, said Gillstedt, a statistician at Sahlgrenska University Hospital’s Department of Dermatology and Venereology.
When the researchers compared different AI models, the differences became clear.
The most advanced model was able to distinguish individuals who subsequently developed melanoma from those who did not in about 73 per cent of cases, compared with about 64 per cent when only age and sex were used.
The combination of diagnoses, medication and sociodemographic data made it possible to identify small, high-risk groups for whom the risk of developing melanoma within five years was around 33 per cent.
“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,” said Sam Polesie, Associate Professor of Dermatology and Venereology at the University of Gothenburg.
More research and policy decisions are needed before the method can be introduced in healthcare. However, the results show that AI models trained on large amounts of registry data can become an important source of support for more personalised risk assessments and future screening strategies for melanoma.
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