Mapping artificial intelligence applications for clinical and operational decision support in prehospital emergency medical services: A scoping review.

BACKGROUND: Artificial intelligence is increasingly explored in prehospital emergency medical services to support clinical and organisational decision-making, yet its real-world application remains unclear. OBJECTIVE: To map the use of artificial intelligence in prehospital emergency medical services, focusing on decision-making processes including dispatch, triage, and transport coordination. METHODS: A scoping review was conducted following Joanna Briggs Institute methodology and reported according to PRISMA-ScR guidelines. PubMed, CINAHL, Scopus, Engineering Source, and INSPEC were searched (March 2025) without time restrictions. Empirical studies addressing the development, validation, or application of artificial intelligence in prehospital settings were included. Data were synthesised using descriptive analysis and iterative thematic grouping. RESULTS: Thirty-seven studies were included, mainly published between 2020 and 2024. Most were observational or proof-of-concept, with machine learning as the predominant approach. Five application domains were identified: time-sensitive conditions, complex emergency management, dispatch and transport coordination, predictive analytics, and organisational efficiency. Artificial intelligence showed potential to improve early diagnosis and operational decision-making; however, most systems lacked external validation and real-world implementation. CONCLUSIONS: Artificial intelligence represents a promising decision-support tool in prehospital emergency care. Nevertheless, evidence remains preliminary, highlighting the need for rigorous validation, integration into clinical workflows, and training to support safe and effective adoption.