Beyond technical efficacy: challenges and critical concerns of large language model’s impact on medical education in China: a systematic review

Abstract Large language model (LLM) advances the medical education process, offering innovative personalized learning approaches or scenarios. This systematic review aims to synthesize updated core themes, evaluate mainstream LLM platforms, highlight concerns regarding the integration of LLM and China’s medical education landscape and compare findings with earlier perspectives. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) guidelines, we reviewed literature from November 2022 to April 2025 from PubMed, Web of Science, CNKI, and Wanfang databases. From 915 records selected, 14 high-impact studies were selected through rigorous screening for relevance and quality. High impact abstracts from large academic conferences were also included in the systematic scoping. The number of LLM supporting Chinese language corpora and corresponding case-based learning (CBL) pedagogies in 2024 experienced a significant improvement. Compared to the previous, LLMs demonstrated better performance in accuracy of answering professional medical questions and multi-modal output capability; whereas, issues of academic dishonesty and privacy leakage, technical or logical errors still captured widespread concerns. While encountering challenges in data security, cultural alignment, and equitable regulation, further updates are needed to improve medical LLM standardization and foster a human-machine collaborative educational ecosystem.