GENERATIVE ARTIFICIAL INTELLIGENCE AND LARGE LANGUAGE MODELS IN EMERGENCY MEDICINE: A NARRATIVE REVIEW

Abstract

The integration of generative artificial intelligence (AI) and large language models (LLMs) into healthcare has accelerated dramatically, with emergency medicine emerging as a particularly dynamic yet challenging domain for clinical deployment. This comprehensive narrative review synthesizes contemporary literature to examine current clinical applications, critical safety considerations, implementation scalability and governance, and future research priorities surrounding generative AI in acute care settings. We examine the current clinical applications, critical safety considerations, implementation scalability and governance, and future research priorities for generative AI in acute care settings. Current emergency medicine applications span automated documentation via ambient clinical intelligence systems, clinical decision support for triage and diagnostic prediction, patient communication tools including discharge summary generation, and multilingual data extraction supporting care transitions. Despite promising efficiency gains - including documented reductions in clinician burnout from 50.6% to 29.4% and modeled documentation time savings of up to 7.1 hours per shift cycle - substantial safety concerns persist, with hallucination rates ranging from 26% to 36% across automated pipelines and systematic misclassification in high-acuity triage tasks. We address automation bias (26% increased risk) and data privacy and governance risks, and identify algorithmic equity as a critical research priority. We conclude that while generative AI holds transformative potential to reduce clerical burden and augment clinical reasoning, its successful deployment in emergency medicine requires rigorous attention to clinical safety, health equity, workflow integration, and human‑factors considerations.