Artificial Intelligence and Machine Learning in Prehospital Emergency Care: A Systematic Scoping Review

Abstract

Introduction: The literature on the use of AI in prehospital emergency care (PEC) settings is scattered and diverse, making it difficult to understand the current state of the field. In this scoping review, we aim to provide a descriptive analysis of the current literature and to visualise and identify knowledge and methodological gaps using an evidence map. Methods: We conducted a scoping review from inception until 14 December 2021 on MEDLINE, Embase, Scopus, IEEE Xplore, ACM Digital Library, and Cochrane Central Register of Controlled Trials (CENTRAL). We included peer-reviewed, original studies that applied AI to prehospital data, including applications for cardiopulmonary resuscitation (CPR), automated external defibrillation (AED), out-of-hospital cardiac arrest, and emergency medical service (EMS) infrastructure like stations and ambulances. Results: The search yielded 4350 articles, of which 106 met the inclusion criteria. Most studies were retrospective (n=88, 83.0%), with only one (0.9%) randomised controlled trial. Studies were mostly internally validated (n=96, 90.6%), and only ten studies (9.4%) reported on calibration metrics. While the most studied AI applications were Triage/Prognostication (n=52, 49.1%) and CPR/AED optimisation (n=26, 24.5%), a few studies reported unique use cases of AI such as patient-trial matching for research and Internet-of-Things (IoT) wearables for continuous monitoring. Out of 49 studies that identified a comparator, 39 reported AI performance superior to either clinicians or non-AI status quo algorithms. The minority of studies utilised multimodal inputs (n=37, 34.9%), with few models using text (n=8), audio (n=5), images (n=1), or videos (n=0) as inputs. Conclusion: AI in PEC is a growing field and several promising use cases have been reported, including prognostication, demand prediction, resource optimisation, and IoT continuous monitoring systems. Prospective, externally validated studies are needed before applications can progress beyond the proof-of-concept stage to real-world clinical settings.

Revolutionizing U.S. Pavement Infrastructure: A pathway to sustainability and resilience through nanotechnology and AI Innovations

“…Literary examination of AI use cases in healthcare (Chee et al, 2023); information and organization management (Qamar et al, 2021), and the possibility of digital records management (Cushing & Osti, 2022) is a great source for the difficulties and advantages of implementing AI in different domains. Through an analysis of the determinants of successful integration of AI solutions in healthcare and the examination of AI role in digital archive skills, researchers can find meaningful themes, trends and research voids in AI-integration in the area of infrastructure management.…”