Stress testing reveals gaps in clinic readiness of image-based diagnostic artificial intelligence models.

Artificial intelligence models match or exceed dermatologists in melanoma image classification. Less is known about their robustness against real-world variations, and clinicians may incorrectly assume that a model with an acceptable area under the receiver operating characteristic curve or related performance metric is ready for clinical use. Here, we systematically assessed the performance of dermatologist-level convolutional neural networks (CNNs) on real-world non-curated images by applying computational "stress tests".

Molecular risk prediction in cutaneous melanoma: A meta-analysis of the 31-gene expression profile prognostic test in 1,479 patients.

Background: Multiple studies have reported on the accuracy of the prognostic 31-gene expression profile test for cutaneous melanoma. Consistency of the test results across studies has not been systematically evaluated.

Objective: To assess the robustness of the prognostic value of the 31-gene expression profile.