From non-agentic large language models to multi-agent systems in emergency medicine: a scoping review

Objective: This study aimed to conduct a scoping review of studies on non-agentic large language models (LLMs), LLM-based agents, and multi-agent systems reported in emergency medicine, and to identify current research trends and major gaps by analyzing their clinical application scope, system structures, evaluation approaches, and input data characteristics. Methods: The Web of Science, Scopus, PubMed, and CINAHL databases were searched for literature published from March 8, 2021, to March 7, 2026. Among English full-text articles, studies addressing the application, evaluation, or benchmarking of non-agentic LLMs, LLM-based agents, or multi-agent systems in emergency medicine or the emergency department (ED) were included. Through reference tracking, 35 studies were analyzed. Results: Of the 35 included studies, 26 were application studies, 6 were framework studies, and 3 were benchmark studies. The studies were concentrated on a limited set of tasks, including triage, diagnostic and treatment decision support, and documentation. In terms of system type, non-agentic LLMs were the most common (n=25), followed by LLM-based agents (n=7) and multi-agent systems (n=3). Inputs were predominantly text-based, and evaluation mainly relied on expert comparison, retrospective record review, vignette-based comparison, and task-specific performance metrics. In contrast, workflow-level, prospective, and safety and trustworthiness-oriented evaluation were limited. Conclusion: LLMs in emergency medicine have shown potential for task-level decision support and documentation. However, current literature remains focused on non-agentic LLM-based task support, while studies reflecting the dynamic workflow of real EDs remain limited. Future research should expand toward workflow-aware design, operational evaluation, multimodal data integration, multi-agent-based role coordination, and safety and trustworthiness validation.