Artificial intelligence in emergency medicine: A scoping review

Abirami Kirubarajan; Ahmed Taher; Shawn Khan; Sameer Masood

doi:10.1002/emp2.12277

Artificial intelligence in emergency medicine: A scoping review

J Am Coll Emerg Physicians Open. 2020 Nov 7;1(6):1691-1702. doi: 10.1002/emp2.12277. eCollection 2020 Dec.

Authors

Abirami Kirubarajan^{1

2}, Ahmed Taher³, Shawn Khan¹, Sameer Masood^{3

4}

Affiliations

¹ Faculty of Medicine University of Toronto Toronto Ontario Canada.
² Institute of Health Policy Management and Evaluation University of Toronto Toronto Ontario Canada.
³ Division of Emergency Medicine, Department of Medicine University of Toronto Toronto Ontario Canada.
⁴ Toronto General Hospital Research Institute University Health Network Toronto Ontario Canada.

Abstract

Introduction: 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.

Methods: 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.

Results: 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.

Conclusion: 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.

Keywords: algorithm; artificial intelligence; artificial neural networks; emergency department; emergency medicine; machine learning; technology.