From nonagentic large language models to multiagent systems in emergency medicine: a scoping review.

objectiveThis scoping review aimed to examine studies of nonagentic large language models (LLMs), LLM-based agents, and multiagent systems in emergency medicine and to identify current research trends and major gaps by analyzing their scope of clinical application, system structures, evaluation approaches, and input data characteristics.

methodsThe Web of Science, Scopus, PubMed, and CINAHL were searched for literature published from March 8, 2021, to March 7, 2026. English-language full-text studies published during this period were included if they addressed the application, evaluation, or benchmarking of nonagentic LLMs, LLM-based agents, or multiagent systems in emergency medicine or the emergency department. Additional studies were identified through reference tracking and supplementary searching. In total, 35 studies were analyzed.

resultsOf the 35 included studies, 26 were application studies, 6 were framework studies, and 3 were benchmark studies. Nonagentic LLMs were the most common system type (n=25), followed by LLM-based agents (n=7) and multiagent 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 evaluations were limited.

conclusionLLMs in emergency medicine have shown potential for task-level decision support and documentation. However, the current literature remains focused on nonagentic LLM-based task support, whereas studies that reflect the dynamic workflow of real emergency departments remain limited. Future research should expand toward workflow-aware design, operational evaluation, multimodal data integration, multiagent-based role coordination, and validation of safety and trustworthiness.