Deep learning for dermatologists: Part I. Fundamental concepts.
artificial intelligence
deep learning
dermatology
machine learning
Journal
Journal of the American Academy of Dermatology
ISSN: 1097-6787
Titre abrégé: J Am Acad Dermatol
Pays: United States
ID NLM: 7907132
Informations de publication
Date de publication:
12 2022
12 2022
Historique:
received:
03
02
2020
revised:
16
04
2020
accepted:
12
05
2020
pubmed:
21
5
2020
medline:
7
12
2022
entrez:
21
5
2020
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 32434009
pii: S0190-9622(20)30921-X
doi: 10.1016/j.jaad.2020.05.056
pmc: PMC7669702
mid: NIHMS1596933
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
1343-1351Subventions
Organisme : NCI NIH HHS
ID : K08 CA215105
Pays : United States
Informations de copyright
Copyright © 2020 American Academy of Dermatology, Inc. Published by Elsevier Inc. All rights reserved.