The top 100 most-cited articles on large language models in medicine: A bibliometric analysis.

OBJECTIVES

Large language models (LLMs) are revolutionizing medical research. However, there is a lack of bibliometric analysis that identifies citation trends shaping the history of this field. This study analyzes the top 100 (T100) most-cited articles on LLMs in medicine to assess their impact and characteristics.

METHODS

A bibliometric analysis of top-cited articles in the Web of Science database using search terms like "LLMs, generative artificial intelligence, GPT" from 2022 to 2025. Two reviewers identified the T100 papers, extracting publication details, citations, and research themes, adhering to BIBLIO reporting guidelines.

RESULTS

The T100 articles had contributed from 655 authors, and 92 articles were published in 2023. Original research constituted the majority of publications (60 articles). Collectively, these works accumulated 14,847 citations, with individual citations ranging from 50 to 1057 (average 148.47). The U.S. led global contributions with 56 articles, Stanford University emerging as the most prolific institution (8 articles). The top seven journals contributed to 31% of the T100, and published the largest share (8 articles) in 70 peer-reviewed journals. The most-cited article is "Evolutionary-scale prediction of atomic-level protein structure with a language model" (Lin et al., Science 2023; 1057 citations). The research themes centered on evaluating LLMs' performance in exam-style evaluations, medical knowledge synthesis, and question-answering tasks in medicine.

CONCLUSION

This analysis provides a core overview of high-impact LLMs research in medicine, guiding future applications. The findings highlighted the remarkable progress in clinical decision support, drug discovery, multimodal medical imaging analysis, and personalized medical information-answering. They also stress the need for prospective trials to assess real-world clinical impacts, boost the reliability of LLMs-generated medical info, develop consensus-driven solutions to address ethical challenges, and launch global initiatives to democratize LLMs tools.