Evidence map›Paper›PMID 41056572›Full record

ReviewJMIR medical education2025

ChatGPT in Medical Education: Bibliometric and Visual Analysis.

Yuning Zhang, Xiaolu Xie, Qi Xu

Abstract readReview
In one paragraph

Review in JMIR medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
  9. Article
  10. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Yuning ZhangSchool of Basic Medical Sciences, Gannan Medical University, Ganzhou, China.ORCID 0009-0006-7692-0083
Xiaolu XieSchool of Medical and Information Engineering, Gannan Medical University, Ganzhou, China.ORCID 0000-0003-0662-4685
Qi XuSchool of Public Health and Health Management, Gannan Medical University, 1 Harmony Avenue, Rongjiang New District, Ganzhou, 341000, China, 86 15770701553.ORCID 0000-0002-7876-9552

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: ChatGPT is a generative artificial intelligence-based chatbot developed by OpenAI. Since its release in the second half of 2022, it has been widely applied across various fields. In particular, the application of ChatGPT in medical education has become a significant trend. To gain a comprehensive understanding of the research developments and trends regarding ChatGPT in medical education, we conducted an extensive review and analysis of the current state of research in this field. Objective: This study used bibliometric and visualization analysis to explore the current state of research and development trends regarding ChatGPT in medical education. Methods: A bibliometric analysis of 407 articles on ChatGPT in medical education published between March 2023 and June 2025 was conducted using CiteSpace, VOSviewer, and Bibliometrix (RTool of RStudio). Visualization of countries, institutions, journals, authors, keywords, and references was also conducted. Results: This bibliometric analysis included a total of 407 studies. Research in this field began in 2023, showing a notable surge in annual publications until June 2025. The United States, China, Türkiye, the United Kingdom, and Canada produced the most publications. Networks of collaboration also formed among institutions. The University of California system was a core research institution, with 3.4% (14/407) of the publications and 0.17 betweenness centrality. BMC Medical Education, Medical Teacher, and the Journal of Medical Internet Research were all among the top 10 journals in terms of both publication volume and citation frequency. The most prolific author was Yavuz Selim Kiyak, who has established a stable collaboration network with Isil Irem Budakoglu and Ozlem Coskun. Author collaboration in this field is usually limited, with most academic research conducted by independent teams and little communication between teams. The most frequent keywords were "AI," "ChatGPT," and "medical education." Keyword analysis further revealed "educational assessment," "exam," and "clinical practice" as current research hot spots. The most cited paper was "Performance of ChatGPT on USMLE: Potential for AI-Assisted Medical Education Using Large Language Models," and the paper with the strongest citation burst was "Are ChatGPT's Knowledge and Interpretation Ability Comparable to Those of Medical Students in Korea for Taking a Parasitology Examination?: A Descriptive Study." Both papers focus on evaluating ChatGPT's performance in medical exams. Conclusions: This study reveals the significant potential of ChatGPT in medical education. As the technology improves, its applications will expand into more fields. To promote the diversification and effectiveness of ChatGPT in medical education, future research should strengthen interregional collaboration and enhance research quality. These findings provide valuable insights for researchers to identify research perspectives and guide future research directions.

Indexed as

BibliometricsEducation, MedicalGenerative Artificial IntelligenceAIartificial intelligencebibliometricChatGPTCiteSpacemedical educationVOSviewer

Identifiers

PMID41056572
PMCPMC12503443

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.