Can large language models serve as digital assistants for medical undergraduates? – A bibliometric mapping and scoping analysis of the medical-education literature

Abstract

Objective: Since the public release of ChatGPT in late 2022, large language models (LLMs) have been rapidly adopted in medical education, and the emergence of the free and open-source Chinese model DeepSeek in 2025 has further accelerated local uptake. However, their academic impact and measurable outcomes remain unclear. This study aims to conduct a comprehensive bibliometric analysis of literature from 1999 to 2025, mapping the evolution, thematic landscape and emerging frontiers of LLM-assisted medical education, with particular attention to the post-ChatGPT phenomenon represented by DeepSeek, in order to clarify current research status and inform future directions for educators and policymakers. Methods: English-language articles and reviews (1999–23/03/2025) were retrieved from the Web of Science Core Collection. Publication trends, co-authorships, co-citations, burst keywords and thematic clusters were analysed with CiteSpace 6.4.R1, R-bibliometrix and VOSviewer 1.6.20. Results: The analysis revealed a sharp growth in publications after 2022, coinciding with the release of ChatGPT, with the United States, China and Canada emerging as leading contributors. Influential institutions included Mayo Clinic, University of Toronto and Karolinska Institutet. Research hotspots clustered around generative artificial intelligence (AI) applications, machine learning, simulation-based training and blended learning. Recent bursts of keywords such as ‘DeepSeek’ indicate expanding regional engagement and diversification of AI tools. Conclusions: Large language model research in medical education is transitioning from exploratory commentaries to more applied and empirical investigations. While open-source models offer opportunities for broader access, critical challenges remain regarding measurable educational outcomes, ethical frameworks and global equity. Addressing these gaps through rigorous outcome-based studies and cross-institutional collaboration will be essential for the sustainable integration of LLMs into medical curricula.