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