Home›Emergency Medicine› Large language models offer potential benefits and significant challenges for emergency medicine education
Large language models offer potential benefits and significant challenges for emergency medicine educationLarge Language Models Offer New Tools for Emergency Medicine Training
Frontiers in MedicinePublished August 6, 2026Study authors: Tingting Fan, Tianle Gao, Wan Tang, Qian Xu, Xingyou Wang, Qiaoli Su, Qingguo LyuDOI ↗Editorial oversight: Dr. Lars van Dijk, PhD · Surgical, Procedural & Diagnostic
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Key Takeaway
Recognize large language models as promising but high-risk tools requiring careful oversight in emergency medicine training.
The authors conducted a narrative review exploring how large language models can be utilized within the context of emergency medicine education. The scope included various applications such as just-in-time tutoring, case drafting, simulation rehearsal, and communication coaching. The primary goal was to identify both the potential benefits and the inherent risks associated with these technologies for learners and educators.
Qualitative findings suggest that large language models can provide more timely feedback, broader access to structured resources, and consistent training for high-acuity scenarios. However, several significant constraints were identified, including the risk of hallucinations, a lack of local protocol alignment, automation bias, and concerns regarding data privacy and cybersecurity. The authors also noted issues with relational authenticity and uneven faculty readiness.
Limitations include the potential for inconsistent accuracy in AI-assisted assessments and multilingual inequities. Because this is a narrative review rather than a primary trial, the results should be interpreted as an overview of current literature. Clinicians and educators may consider these tools as supplemental aids that require local grounding and oversight to ensure safety and educational integrity.
A review of 53 studies looked at how large language models, or LLMs, can be used to teach emergency medicine. These tools can help with several tasks, such as creating study materials, drafting cases, and providing feedback on communication skills. The goal is to give students more consistent training for high-pressure situations.
While these tools offer benefits like quicker access to information and practice for rare medical scenarios, there are important risks to consider. These include the risk of AI generating incorrect information, a lack of local context in its responses, and concerns about data privacy. Some experts also worry that students might rely too much on automated systems.
Because this is a narrative review of existing literature rather than a clinical trial, the findings are not yet ready to change standard medical practices. The results suggest that while AI has potential for training, it should be used with careful oversight and local guidance to ensure safety and accuracy.
What this means for you:
AI tools show promise for emergency medicine training but require human oversight to manage risks like inaccurate information.
Common questions
What are the benefits of using AI in medical training?
AI tools can offer several benefits for emergency medicine learners. These include more timely feedback, easier access to structured teaching resources, and repeated practice for high-acuity scenarios that do not happen often. They can also help provide more consistent training for communication skills.
What are the risks of using these AI models?
There are several risks when using large language models in education. These include hallucination, where the AI provides incorrect information, and a mismatch with local medical protocols. Other concerns include privacy issues, cybersecurity risks, and the potential for students to rely too much on automated systems.
Is AI ready to replace human instructors?
The evidence suggests that while AI has promise for training, it is not a replacement for standard care. Because of issues like limited relational authenticity and uncertain validity in assessments, experts suggest a phased approach with human oversight and local grounding.
Study Details
Study typeSystematic review
EvidenceLevel 1
PublishedAug 2026
View Original Abstract ↓
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.
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.
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.
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.
