ReviewFrontiers in public health2026
Large language models in emergency medicine education: opportunities, challenges, and implementation pathways.
Review in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Emergency medicine education occurs in high-acuity, interruption-prone, and time-constrained environments, where learners must develop rapid clinical reasoning, effective communication, procedural competence, and reliable documentation skills. Large language models (LLMs) are increasingly being explored in health-professions education. This narrative review synthesizes emerging applications, major risks, and implementation pathways for LLMs in emergency medicine education. Methods: A structured narrative review was conducted using PubMed, Web of Science Core Collection, China National Knowledge Infrastructure (CNKI), and Wanfang Data. The search period extended from January 1, 2023, to April 10, 2026. English and Chinese search blocks combined terms related to LLMs or generative artificial intelligence, emergency medicine or emergency care contexts, and education, training, simulation, assessment, communication, documentation, or implementation. After duplicate removal, title and abstract screening, and full-text review, 48 English-language studies and 5 Chinese-language studies were included. Eligible records addressed emergency medicine or emergency medical services education, simulation or virtual-patient applications, formative assessment and feedback, documentation or discharge communication, or governance issues relevant to educational use in emergency settings. Results: LLMs showed potential across multiple educational domains in emergency medicine, including just-in-time tutoring, resource generation, case drafting, simulation and virtual-patient rehearsal, formative feedback support, examination and competency-assessment support, documentation and discharge communication coaching, and educator workflow support. Potential benefits included more timely feedback, broader access to structured teaching resources, repeated rehearsal of low-frequency high-acuity scenarios, and greater consistency in communication training. Translation into routine educational practice remains constrained by hallucination, context mismatch with local protocols, automation bias, limited relational authenticity in AI-mediated interaction, privacy and cybersecurity concerns, multilingual inequity, uncertain validity of AI-assisted assessment, and uneven faculty readiness. Conclusion: LLMs hold substantial promise for strengthening emergency medicine education, particularly in areas requiring rapid language-based support, structured feedback, scalable case generation, and communication rehearsal. Current evidence supports phased adoption, local grounding in institutional protocols, secure workflows, explicit faculty oversight, and evaluation of educational, operational, and governance outcomes. This review proposes a pragmatic framework for the integration of LLMs into emergency medicine education.
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.