Computer vision has promise in image-based cutaneous melanoma diagnosis but clinical utility is uncertain.

To determine if computer algorithms from an international melanoma detection challenge can improve dermatologist melanoma diagnostic accuracy.

Cross-sectional study using 150 dermoscopy images (50 melanomas, 50 nevi, 50 seborrheic keratoses) from the test dataset of a melanoma detection challenge, along with algorithm results from twenty-three teams. Eight dermatologists and nine dermatology residents classified dermoscopic lesion images in an online reader study and provided their confidence level.

The top-ranked computer algorithm had a ROC area of 0.87, which was higher than the dermatologists (0.74) and the residents (0.66) (p<0.001 for all comparisons). At the dermatologists' overall sensitivity in classification of 76.0%, the algorithm had a superior specificity (85.0% vs. 72.6%, p=0.001). Imputation of computer algorithm classifications for dermatologist evaluations with low confidence ratings (26.6% of evaluations) increased dermatologist sensitivity from 76.0% to 80.8% and specificity from 72.6% to 72.8%.

Artificial study setting lacking the full spectrum of skin lesions as well as clinical metadata.

Accumulating evidence suggests that deep neural networks can classify skin images of melanoma and its benign mimickers with high accuracy and potentially improve human performance.