A deep learning system for differential diagnosis of skin diseases

Yuan Liu
Ayush Jain
Clara Eng
David Way
Kang Lee
Peggy Bui
Kimberly Kanada
Guilherme de Oliveira Marinho
Jess Gallegos
Sara Gabriele
Vishakha Gupta
Nalini Singh
Lily Peng
Dennis Ai
Susan Huang
Carter Dunn
Nature Medicine(2020)


Skin conditions affect 1.9 billion people. Because of a shortage of dermatologists, most cases are seen instead by general practitioners with lower diagnostic accuracy. We present a deep learning system (DLS) to provide a differential diagnosis of skin conditions using 16,114 de-identified cases (photographs and clinical data) from a teledermatology practice serving 17 sites. The DLS distinguishes between 26 common skin conditions, representing 80% of cases seen in primary care, while also providing a secondary prediction covering 419 skin conditions. On 963 validation cases, where a rotating panel of three board-certified dermatologists defined the reference standard, the DLS was non-inferior to six other dermatologists and superior to six primary care physicians (PCPs) and six nurse practitioners (NPs) (top-1 accuracy: 0.66 DLS, 0.63 dermatologists, 0.44 PCPs and 0.40 NPs). These results highlight the potential of the DLS to assist general practitioners in diagnosing skin conditions.