大语言模型与模型上下文协议如何重塑医学教育的未来OA

This paper systematically explores the profound transformation of the medical edu-cation industry driven by the integration of Large Language Models(LLMs)and the Model Context Protocol(MCP).It highlights that while LLMs demonstrate significant potential in medical education,they face a core limitation:the lack of real-time,reliable contextual information.As an open standard protocol,MCP addresses this gap by connecting AI with external data sources,such as Electronic Health Records(EHRs)and Learning Management Systems(LMS),to construct a"nervous system"for LLMs,effectively compensating for their deficiencies in contextual awareness.The paper provides a detailed analysis of how the"LLM+MCP"paradigm reshapes medical education across four dimen-sions including high-fidelity clinical simulation,personalized learning support,full-cycle competency assessment,and dynamic evidence-based medical assistance.Simultaneously,the study identifies practical challenges,including technology integration,data security,ethical biases,and educational equality,envisioning a future for medical education characterized by data-driven methodologies,human-machine collaboration,and an emphasis on clinical reasoning and lifelong learning.