Deep learning for dermatologists: Part II. Current applications.
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:
01
02
2020
revised:
07
05
2020
accepted:
08
05
2020
pubmed:
20
5
2020
medline:
7
12
2022
entrez:
20
5
2020
Statut:
ppublish
Résumé
Because 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.
Identifiants
pubmed: 32428608
pii: S0190-9622(20)30918-X
doi: 10.1016/j.jaad.2020.05.053
pmc: PMC7669658
mid: NIHMS1596936
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
1352-1360Subventions
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.