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

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

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

A total of 137 outof the 2175 citations in the original database were eligible for full-text evaluation. Of these articles, 47 were included inthe scoping review and considered for theme extraction. This review covers seven main areas of AI techniques in emer-gency 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 departmentmanagement (17.03%), the future impact of AI on Emergency Medical Services (EMS) (8.51%), and ethical issues (8.51%).

There has been a rapid increase in AI research in emergency medicine in recent years. Several studies havedemonstrated the potential of AI in diverse contexts, particularly when improving patient outcomes through predic-tive modelling. According to the synthesis of studies in our review, AI-based decision-making lacks transparency. Thisfeature makes AI decision-making opaque.