The Application of Machine Learning to Quality Improvement Through the Lens of the Radiology Value Network.
Machine learning
artificial intelligence
data aggregation
radiology quality improvement
radiology value network
Journal
Journal of the American College of Radiology : JACR
ISSN: 1558-349X
Titre abrégé: J Am Coll Radiol
Pays: United States
ID NLM: 101190326
Informations de publication
Date de publication:
Sep 2019
Sep 2019
Historique:
received:
20
05
2019
accepted:
22
05
2019
entrez:
8
9
2019
pubmed:
8
9
2019
medline:
20
8
2020
Statut:
ppublish
Résumé
Recent advances in machine learning and artificial intelligence offer promising applications to radiology quality improvement initiatives as they relate to the radiology value network. Coordination within the interlocking web of systems, events, and stakeholders in the radiology value network may be mitigated though standardization, automation, and a focus on workflow efficiency. In this article the authors present applications of these various strategies via use cases for quality improvement projects at different points in the radiology value network. In addition, the authors discuss opportunities for machine-learning applications in data aggregation as opposed to traditional applications in data extraction.
Identifiants
pubmed: 31492403
pii: S1546-1440(19)30639-8
doi: 10.1016/j.jacr.2019.05.039
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
1254-1258Informations de copyright
Copyright © 2019 American College of Radiology. Published by Elsevier Inc. All rights reserved.