• Dennis H Murphree, Pranav Puri, Huma Shamim, Spencer A Bezalel, Lisa A Drage, Michael Wang, Mark R Pittelkow, Rickey E Carter, DavisMark D PMDPMayo Clinic Office of Artificial Intelligence in Dermatology; Department of Dermatology, Mayo Clinic, Rochester, Minnesota., Alina G Bridges, Aaron R Mangold, James A Yiannias, Megha M Tollefson, Julia S Lehman, Alexander Meves, Clark C Otley, Olayemi Sokumbi, Matthew R Hall, and Nneka Comfere.
• Department of Health Sciences Research, Division of Digital Health Sciences, Mayo Clinic, Rochester, Minnesota; Mayo Clinic Office of Artificial Intelligence in Dermatology. Electronic address: murphree.dennis@mayo.edu.
• J. Am. Acad. Dermatol. 2020 May 17.

AbstractArtificial intelligence is generating substantial interest in the field of medicine. One form of artificial intelligence, deep learning, has led to rapid advances in automated image analysis. In 2017, an algorithm demonstrated the ability to diagnose certain skin cancers from clinical photographs with the accuracy of an expert dermatologist. Subsequently, deep learning has been applied to a range of dermatology applications. Although experts will never be replaced by artificial intelligence, it will certainly affect the specialty of dermatology. In this first article of a 2-part series, the basic concepts of deep learning will be reviewed with the goal of laying the groundwork for effective communication between clinicians and technical colleagues. In part 2 of the series, the clinical applications of deep learning in dermatology will be reviewed and limitations and opportunities will be considered.Copyright © 2020 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

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