A Systematic Literature Review of Artificial Intelligence in Prehospital Emergency Care

Abstract

Background: The emergency medical services (EMS) sector, as a complex system, presents substantial hurdles in providing excellent treatment while operating within limited resources, prompting greater adoption of artificial intelligence (AI) as a tool for improving operational efficiency. While AI models have proved beneficial in healthcare operations, there is limited explainability and interpretability, as well as a lack of data used in their application and technological advancement. Methods: The scoping review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for scoping reviews, using PubMed, IEEE Xplore, and Web of Science, with a procedure of double screening and extraction. The search included articles published from 2018 to the beginning of 2025. Studies were excluded if they did not explicitly identify an artificial intelligence (AI) component, lacked relevance to emergency department (ED) or prehospital contexts, failed to report measurable outcomes or evaluations, or did not exploit real-world data. We analyzed the data source used, clinical subclasses, AI domains, ML algorithms, their performance, as well as potential roles for large language models (LLMs) in future applications. Results: A comprehensive PRISMA-guided methodology was used to search academic databases, finding 1181 papers on prehospital emergency treatment from 2018 to 2025, with 65 articles identified after an extensive screening procedure. The results reveal a significant increase in AI publications. A notable technological advancement in the application of AI in EMS using different types of data was explored. Conclusions: These findings highlighted that AI and ML have emerged as revolutionary innovations with huge potential in the fields of healthcare and medicine. There are several promising AI interventions that can improve prehospital emergency care, particularly for out-of-hospital cardiac arrest and triage prioritization scenarios. Implications for EMS Practice: Integrating AI methods into prehospital care can optimize the use of available resources, as well as triage and dispatch efficiency. LLMs may have the potential to improve understanding and assist in decision-making under pressure in emergency situations by combining various forms of recorded data. However, there is a need to emphasize continued research and strong collaboration between AI experts and EMS physicians to ensure the safe, ethical, and effective integration of AI into EMS practice.

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