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Deep learning for dermatologists: Part I. Fundamental concepts.
Murphree, Dennis H; Puri, Pranav; Shamim, Huma; Bezalel, Spencer A; Drage, Lisa A; Wang, Michael; Pittelkow, Mark R; Carter, Rickey E; Davis, Mark D P; Bridges, Alina G; Mangold, Aaron R; Yiannias, James A; Tollefson, Megha M; Lehman, Julia S; Meves, Alexander; Otley, Clark C; Sokumbi, Olayemi; Hall, Matthew R; Comfere, Nneka.
Afiliação
  • Murphree DH; 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.
  • Puri P; Mayo Clinic Office of Artificial Intelligence in Dermatology; Mayo Clinic Alix School of Medicine, Scottsdale, Arizona.
  • Shamim H; Mayo Clinic Office of Artificial Intelligence in Dermatology; Department of Dermatology, Mayo Clinic, Rochester, Minnesota.
  • Bezalel SA; Mayo Clinic Office of Artificial Intelligence in Dermatology; Department of Dermatology, Mayo Clinic, Rochester, Minnesota.
  • Drage LA; Mayo Clinic Office of Artificial Intelligence in Dermatology; Department of Dermatology, Mayo Clinic, Rochester, Minnesota.
  • Wang M; Department of Dermatology, University of California San Francisco, San Francisco, California.
  • Pittelkow MR; Mayo Clinic Office of Artificial Intelligence in Dermatology; Department of Dermatology, Mayo Clinic, Scottsdale, Arizona.
  • Carter RE; Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Jacksonville, Florida.
  • Davis MDP; Mayo Clinic Office of Artificial Intelligence in Dermatology; Department of Dermatology, Mayo Clinic, Rochester, Minnesota.
  • Bridges AG; Mayo Clinic Office of Artificial Intelligence in Dermatology; Department of Dermatology, Mayo Clinic, Rochester, Minnesota; Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota.
  • Mangold AR; Mayo Clinic Office of Artificial Intelligence in Dermatology; Department of Dermatology, Mayo Clinic, Scottsdale, Arizona.
  • Yiannias JA; Department of Dermatology, Mayo Clinic, Scottsdale, Arizona.
  • Tollefson MM; Mayo Clinic Office of Artificial Intelligence in Dermatology; Department of Dermatology, Mayo Clinic, Rochester, Minnesota.
  • Lehman JS; Mayo Clinic Office of Artificial Intelligence in Dermatology; Department of Dermatology, Mayo Clinic, Rochester, Minnesota; Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota.
  • Meves A; Mayo Clinic Office of Artificial Intelligence in Dermatology; Department of Dermatology, Mayo Clinic, Rochester, Minnesota.
  • Otley CC; Mayo Clinic Office of Artificial Intelligence in Dermatology; Department of Dermatology, Mayo Clinic, Rochester, Minnesota.
  • Sokumbi O; Mayo Clinic Office of Artificial Intelligence in Dermatology; Department of Dermatology, Mayo Clinic, Jacksonville, Florida; Department of Laboratory Medicine and Pathology, Mayo Clinic, Jacksonville, Florida.
  • Hall MR; Mayo Clinic Office of Artificial Intelligence in Dermatology; Department of Dermatology, Mayo Clinic, Jacksonville, Florida.
  • Comfere N; Mayo Clinic Office of Artificial Intelligence in Dermatology; Department of Dermatology, Mayo Clinic, Rochester, Minnesota; Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota.
J Am Acad Dermatol ; 87(6): 1343-1351, 2022 12.
Article em En | MEDLINE | ID: mdl-32434009
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.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Cutâneas / Aprendizado Profundo Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Cutâneas / Aprendizado Profundo Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article