Lloyd Steele, M.B.Ch.B., from the University of London, and colleagues assessed the performance of a machine learning model for Merkel cell carcinoma (MCC) and amelanotic melanoma. The performance of a direct-to-consumer model, which is available in Europe as a certified medical device, was assessed using a set of images that included 28 MCCs, 35 amelanotic melanomas, 28 seborrheic keratoses, and 25 hemangiomas.

The researchers found that the direct-to-consumer app incorrectly classified five of 28 MCCs (17.9 percent) and seven of 35 amelanotic melanomas (22.9 percent) as low risk. Nearly two-thirds of benign lesions (62.2 percent) were classified as high risk. The model's sensitivity for detecting malignancy was 79.4 percent, with a specificity of 37.7 percent.

"In order to improve, machine learning model evaluations should consider the spectrum of diseases that will be seen in practice," Steele said in a statement. "At the moment, most of the performance of those models is driven by the imaging data available, which is particularly scarce when it comes to rare skin cancers."

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