Artificial intelligence in emergency medicine: A scoping review.
algorithm
artificial intelligence
artificial neural networks
emergency department
emergency medicine
machine learning
technology
Journal
Journal of the American College of Emergency Physicians open
ISSN: 2688-1152
Titre abrégé: J Am Coll Emerg Physicians Open
Pays: United States
ID NLM: 101764779
Informations de publication
Date de publication:
Dec 2020
Dec 2020
Historique:
received:
18
04
2020
revised:
04
09
2020
accepted:
22
09
2020
entrez:
4
1
2021
pubmed:
5
1
2021
medline:
5
1
2021
Statut:
epublish
Résumé
Despite the growing investment in and adoption of artificial intelligence (AI) in medicine, the applications of AI in an emergency setting remain unclear. This scoping review seeks to identify available literature regarding the applications of AI in emergency medicine. The scoping review was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for scoping reviews using Medline-OVID, EMBASE, CINAHL, and IEEE, with a double screening and extraction process. The search included articles published until February 28, 2020. Articles were excluded if they did not self-classify as studying an AI intervention, were not relevant to the emergency department (ED), or did not report outcomes or evaluation. Of the 1483 original database citations, 395 were eligible for full-text evaluation. Of these articles, a total of 150 were included in the scoping review. The majority of included studies were retrospective in nature (n = 124, 82.7%), with only 3 (2.0%) prospective controlled trials. We found 37 (24.7%) interventions aimed at improving diagnosis within the ED. Among the 150 studies, 19 (12.7%) focused on diagnostic imaging within the ED. A total of 16 (10.7%) studies were conducted in the out-of-hospital environment (eg, emergency medical services, paramedics) with the remainder occurring either in the ED or the trauma bay. Of the 24 (16%) studies that had human comparators, there were 12 (8%) studies in which AI interventions outperformed clinicians in at least 1 measured outcome. AI-related research is rapidly increasing in emergency medicine. There are several promising AI interventions that can improve emergency care, particularly for acute radiographic imaging and prediction-based diagnoses. Higher quality evidence is needed to further assess both short- and long-term clinical outcomes.
Identifiants
pubmed: 33392578
doi: 10.1002/emp2.12277
pii: EMP212277
pmc: PMC7771825
doi:
Types de publication
Journal Article
Langues
eng
Pagination
1691-1702Informations de copyright
© 2020 The Authors. JACEP Open published by Wiley Periodicals LLC on behalf of the American College of Emergency Physicians.
Déclaration de conflit d'intérêts
The authors have no conflicts of interest to disclose.
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