• Pranav Puri, Nneka Comfere, Lisa A Drage, Huma Shamim, Spencer A Bezalel, Mark R Pittelkow, DavisMark D PMDPMayo Clinic Office of Artificial Intelligence in Dermatology, Rochester, Minnesota; Department of Dermatology, Mayo Clinic, Rochester, Minnesota., Michael Wang, Aaron R Mangold, Megha M Tollefson, Julia S Lehman, Alexander Meves, James A Yiannias, Clark C Otley, Rickey E Carter, Olayemi Sokumbi, Matthew R Hall, Alina G Bridges, and Dennis H Murphree.
• Mayo Clinic Alix School of Medicine, Scottsdale, Arizona; Mayo Clinic Office of Artificial Intelligence in Dermatology, Rochester, Minnesota.
• J. Am. Acad. Dermatol. 2020 May 16.

AbstractBecause of a convergence of the availability of large data sets, graphics-specific computer hardware, and important theoretical advancements, artificial intelligence has recently contributed to dramatic progress in medicine. One type of artificial intelligence known as deep learning has been particularly impactful for medical image analysis. Deep learning applications have shown promising results in dermatology and other specialties, including radiology, cardiology, and ophthalmology. The modern clinician will benefit from an understanding of the basic features of deep learning to effectively use new applications and to better gauge their utility and limitations. In this second article of a 2-part series, we review the existing and emerging clinical applications of deep learning in dermatology and discuss future opportunities and limitations. Part 1 of this series offered an introduction to the basic concepts of deep learning to facilitate effective communication between clinicians and technical experts.Copyright © 2020 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.

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