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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. [...] Our DLS can potentially augment the current clinical workflow in a primary-care setting in several ways.
Juliette Decugis's insight:
Using the Tensorflow framework, Yuan Liu and Ayush Jain's team reached new steps in medical deep learning. Their deep learning system not only categorizes skin conditions as benign or malignant, it successfully identifies 1 out of 26 skin conditions and provides a complete diagnosis for each patient. Its results compare to those of a dermatologist. How can we explain this success? The DLS relies on multiple images per case as well as metadata (+4% accuracy observed) to make predictions. Furthermore, the system makes two level predictions based on the rarity of skin conditions. It's innovative in its data utility as well as system architecture. 
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