BACKGROUND: Computer vision may aid in melanoma detection.
OBJECTIVE: We sought to compare melanoma diagnostic accuracy of computer 
algorithms to dermatologists using dermoscopic images.
METHODS: We conducted a cross-sectional study using 100 randomly selected 
dermoscopic images (50 melanomas, 44 nevi, and 6 lentigines) from an 
international computer vision melanoma challenge dataset (n = 379), along with 
individual algorithm results from 25 teams. We used 5 methods (nonlearned and 
machine learning) to combine individual automated predictions into "fusion" 
algorithms. In a companion study, 8 dermatologists classified the lesions in the 
100 images as either benign or malignant.
RESULTS: The average sensitivity and specificity of dermatologists in 
classification was 82% and 59%. At 82% sensitivity, dermatologist specificity 
was similar to the top challenge algorithm (59% vs. 62%, P = .68) but lower than 
the best-performing fusion algorithm (59% vs. 76%, P = .02). Receiver operating 
characteristic area of the top fusion algorithm was greater than the mean 
receiver operating characteristic area of dermatologists (0.86 vs. 0.71, 
P = .001).
LIMITATIONS: The dataset lacked the full spectrum of skin lesions encountered in 
clinical practice, particularly banal lesions. Readers and algorithms were not 
provided clinical data (eg, age or lesion history/symptoms). Results obtained 
using our study design cannot be extrapolated to clinical practice.
CONCLUSION: Deep learning computer vision systems classified melanoma dermoscopy 
images with accuracy that exceeded some but not all dermatologists.