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. 2023 May 11;11(1):e38.
doi: 10.22037/aaem.v11i1.1974. eCollection 2023.

The Aspects of Running Artificial Intelligence in Emergency Care; a Scoping Review

Mohsen Masoumian Hosseini  1 , Seyedeh Toktam Masoumian Hosseini  2 , Karim Qayumi  3 , Soleiman Ahmady  4 , Hamid Reza Koohestani  5
Affiliations

The Aspects of Running Artificial Intelligence in Emergency Care; a Scoping Review

Mohsen Masoumian Hosseini et al. Arch Acad Emerg Med. .

Abstract

Introduction: Artificial Inteligence (AI) application in emergency medicine is subject to ethical and legal inconsistencies. The purposes of this study were to map the extent of AI applications in emergency medicine, to identify ethical issues related to the use of AI, and to propose an ethical framework for its use.

Methods: A comprehensive literature collection was compiled through electronic databases/internet search engines (PubMed, Web of Science Platform, MEDLINE, Scopus, Google Scholar/Academia, and ERIC) and reference lists. We considered studies published between 1 January 2014 and 6 October 2022. Articles that did not self-classify as studies of an AI intervention, those that were not relevant to Emergency Departments (EDs), and articles that did not report outcomes or evaluations were excluded. Descriptive and thematic analyses of data extracted from the included articles were conducted.

Results: A total of 137 out of the 2175 citations in the original database were eligible for full-text evaluation. Of these articles, 47 were included in the scoping review and considered for theme extraction. This review covers seven main areas of AI techniques in emergency medicine: Machine Learning (ML) Algorithms (10.64%), prehospital emergency management (12.76%), triage, patient acuity and disposition of patients (19.15%), disease and condition prediction (23.40%), emergency department management (17.03%), the future impact of AI on Emergency Medical Services (EMS) (8.51%), and ethical issues (8.51%).

Conclusion: There has been a rapid increase in AI research in emergency medicine in recent years. Several studies have demonstrated the potential of AI in diverse contexts, particularly when improving patient outcomes through predictive modelling. According to the synthesis of studies in our review, AI-based decision-making lacks transparency. This feature makes AI decision-making opaque.

Keywords: Algorithms; Artificial intelligence; Emergency medicine; Emergency service; Ethics; Machine learning; Neural networks; computer; hospital.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
An overview of the manuscript selection process based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). ED: emergency department
Figure 2
Figure 2
Study types included in this review based on the method and application of artificial intelligence in emergency department
Figure 3
Figure 3
An overview of the AI algorithms used in the studies
Figure 4
Figure 4
The applications of artificial intelligence (AI) presented in this overview in emergency department (ED). ML: machine learning; DL: deep learning; PTSD: Post-traumatic stress disorder; EMS: emergency medical services
Figure 5
Figure 5
Proposal for an ethical framework for the use of artificial intelligence (AI) in healthcare systems
See this image and copyright information in PMC

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