ReviewDigital health
Can large language models serve as digital assistants for medical undergraduates? - A bibliometric mapping and scoping analysis of the medical-education literature.
Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Large Language Model Performance on Multistep Clinical Cases: Comparative Study Across Question and Case Levels.JMIR medical education · 2026Article
- Hotspot Evolution and Future Prospects of Large Language Models in Medical Education: A Bibliometric Analysis.Advances in medical education and practice · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.