Broadcasting from the USA: Deep Learning Improves Diagnostic Accuracy for Skin Diseases

Deep learning-aided decision support improves diagnostic accuracy for skin disease, according to a study published online Feb. 5 in Nature Medicine.

Matthew Groh, Ph.D., from the Northwestern University Kellogg School of Management in Evanston, Illinois, and colleagues presented results from a study involving 389 board-certified dermatologists and 459 primary care physicians, which assessed the accuracy of diagnoses submitted by physicians in a store-and-forward teledermatology simulation. Physicians were asked to submit up to four differential diagnoses for 364 images spanning 46 skin diseases.

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