A Review of Perceptual Expertise in Radiology-How it develops, How we can test it, and Why humans still matter in the era of Artificial Intelligence.
Artificial intelligence
Attention
Expertise
Gist
Holistic processing
Perceptual learning
Radiology
Visual perception
Visual search
Journal
Academic radiology
ISSN: 1878-4046
Titre abrégé: Acad Radiol
Pays: United States
ID NLM: 9440159
Informations de publication
Date de publication:
01 2020
01 2020
Historique:
received:
18
07
2019
revised:
26
08
2019
accepted:
27
08
2019
entrez:
11
12
2019
pubmed:
11
12
2019
medline:
4
11
2020
Statut:
ppublish
Résumé
As the first step in image interpretation is detection, an error in perception can prematurely end the diagnostic process leading to missed diagnoses. Because perceptual errors of this sort-"failure to detect"-are the most common interpretive error (and cause of litigation) in radiology, understanding the nature of perceptual expertise is essential in decreasing radiology's long-standing error rates. In this article, we review what constitutes a perceptual error, the existing models of radiologic image perception, the development of perceptual expertise and how it can be tested, perceptual learning methods in training radiologists, and why understanding perceptual expertise is still relevant in the era of artificial intelligence. Adding targeted interventions, such as perceptual learning, to existing teaching practices, has the potential to enhance expertise and reduce medical error.
Identifiants
pubmed: 31818384
pii: S1076-6332(19)30440-4
doi: 10.1016/j.acra.2019.08.018
pii:
doi:
Types de publication
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
26-38Subventions
Organisme : NEI NIH HHS
ID : R01 EY031971
Pays : United States
Informations de copyright
Copyright © 2019 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.