With advanced language processing abilities and broad potential application scope, large language models (LLMs) such as ChatGPT, are driving a new wave of natural language processing in the medical field.

This study aims to identify research hotspots, topic distribution, and future trends of medical LLMs using bibliometric analysis.

A systematic search was conducted across the Web of Science, CNKI, Wanfang Data, and VIP databases for literature on medical LLMs published between January 2017 and June 2024. CiteSpace software was used to extract thematic keywords and other information from the literature, analyze and compare the evolution, hotspots, and trends of domestic and international research.

A total of 1 071 relevant papers were included, revealing that international research mainly focuses on applying artificial intelligence, LLMs, deep learning, and knowledge graphs in medicine. In contrast, domestic research is more limited, focusing on developing Chinese medical question-answering systems and solving unstructured medical data problems.

It is recommended to enhance medical data mining, broaden its application in various scenarios, and leverage international experiences in fine-tuning and evaluating LLMs to advance medical LLM development in China.

由于其强大的语言处理能力和广泛的应用潜力，以ChatGPT为代表的大语言模型引领了医疗领域自然语言处理的新趋势。

本研究通过文献计量分析揭示2017年以来医疗大语言模型的研究热点、主题分布及未来发展方向。

通过Web of Science、中国知网、万方数据知识服务平台和维普网数据库，系统检索和筛选2017年1月—2024年6月关于医疗大语言模型的文献。利用CiteSpace软件提取文献中的主题关键词等信息，分析并对比国内外研究的演进、热点和趋势。

共纳入1 071篇相关文献，结果显示国外研究集中于人工智能、大语言模型、深度学习、知识图谱等技术在医学中的应用，而国内研究则相对较少，侧重于中文医学问答系统构建和医疗数据非结构化问题处理。

深化医疗数据挖掘，拓展多场景应用，并借鉴国际大语言模型的微调和应用评估经验，促进我国医疗大语言模型技术的发展和医学领域应用。