Deep learning for dermatologists: Part I. Fundamental concepts

Dennis H. Murphree, Pranav Puri, Huma Shamim, Spencer A. Bezalel, Lisa A. Drage, Michael Wang, Mark R. Pittelkow, Rickey E. Carter, Mark D.P. Davis, 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, Nneka Comfere

Research output: Contribution to journalReview articlepeer-review

Abstract

Artificial 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.

Original languageEnglish (US)
JournalJournal of the American Academy of Dermatology
DOIs
StateAccepted/In press - 2021

Keywords

  • artificial intelligence
  • deep learning
  • dermatology
  • machine learning

ASJC Scopus subject areas

  • Dermatology

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