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AI identifies early risk patterns for skin cancer
A massive Swedish study shows that AI can spot people at higher risk of melanoma using routine health data. Advanced models significantly outperformed basic methods, identifying high-risk groups with striking accuracy. Some individuals flagged by the system had up to a 33% chance of developing melan…
The study doesn't clarify how many actual melanoma cases were correctly identified versus false positives, which would be crucial for understanding the clinical utility of this approach. It's concerning that the AI was trained primarily on data from lighter-skinned patients, since melanoma affects people of all skin tones and the model's effectiveness across different demographics remains unclear.
The paper actually breaks down the results by sensitivity and specificity rates, showing 87% sensitivity with a 23% false positive rate - those false positives are concerning enough that I'd want to see how the model performs in real clinical workflows before trusting it for screening. The authors don't adequately address how this false positive rate would impact patient follow-up costs and anxiety levels.
The authors don't break down the specificity rates, but their validation on a separate dataset with blinded pathologists suggests they're not just flagging everything as positive. Still, without knowing the false positive rate, it's hard to say whether this would be practical for routine screening or if it would just create more anxiety than actual benefit for dermatologists.