Artificial intelligence in ophthalmology: The path to the real-world clinic.
artificial intelligence
clinical translation
deep learning
eye diseases
machine learning
ophthalmology
real world
Journal
Cell reports. Medicine
ISSN: 2666-3791
Titre abrégé: Cell Rep Med
Pays: United States
ID NLM: 101766894
Informations de publication
Date de publication:
18 07 2023
18 07 2023
Historique:
received:
03
11
2022
revised:
17
04
2023
accepted:
07
06
2023
medline:
21
7
2023
pubmed:
30
6
2023
entrez:
29
6
2023
Statut:
ppublish
Résumé
Artificial intelligence (AI) has great potential to transform healthcare by enhancing the workflow and productivity of clinicians, enabling existing staff to serve more patients, improving patient outcomes, and reducing health disparities. In the field of ophthalmology, AI systems have shown performance comparable with or even better than experienced ophthalmologists in tasks such as diabetic retinopathy detection and grading. However, despite these quite good results, very few AI systems have been deployed in real-world clinical settings, challenging the true value of these systems. This review provides an overview of the current main AI applications in ophthalmology, describes the challenges that need to be overcome prior to clinical implementation of the AI systems, and discusses the strategies that may pave the way to the clinical translation of these systems.
Identifiants
pubmed: 37385253
pii: S2666-3791(23)00214-8
doi: 10.1016/j.xcrm.2023.101095
pmc: PMC10394169
pii:
doi:
Types de publication
Journal Article
Review
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
101095Informations de copyright
Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of interests The authors declare no competing interests.
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