A bibliometric analysis of the rise of ChatGPT in medical research
Open accessAbstract
The rapid emergence of publicly accessible artificial intelligence platforms such as large language models (LLMs) has led to an equally rapid increase in articles exploring their potential benefits and risks. We performed a bibliometric analysis of ChatGPT literature in medicine and science to better understand publication trends and knowledge gaps. Following title, abstract, and keyword searches of PubMed, Embase, Scopus, and Web of Science databases for ChatGPT articles published in the medical field, articles were screened for inclusion and exclusion criteria. Data were extracted from included articles, with citation counts obtained from PubMed and journal metrics obtained from Clarivate Journal Citation Reports. After screening, 267 articles were included in the study, most of which were editorials or correspondence with an average of 7.5 +/- 18.4 citations per publication. Published articles on ChatGPT were authored largely in the United States, India, and China. The topics discussed included use and accuracy of ChatGPT in research, medical education, and patient counseling. Among non-surgical specialties, radiology published the most ChatGPT-related articles, while plastic surgery published the most articles among surgical specialties. The average citation number among the top 20 most-cited articles was 60.1 +/- 35.3. Among journals with the most ChatGPT-related publications, there were on average 10 +/- 3.7 publications. Our results suggest that managing the inevitable ethical and safety issues that arise with the implementation of LLMs will require further research exploring the capabilities and accuracy of ChatGPT, to generate policies guiding the adoption of artificial intelligence in medicine and science.
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The issue of transparency and interpretability is even more pronounced with some powerful generative models, where technical details, model weights, and datasets are proprietary and undisclosed.
“This issue of transparency/interpretability is even more pronounced with some of the powerful generative models, where technical details, model weights, and datasets are proprietary and undisclosed.6”
From Advancing methodological development of artificial intelligence in patient-centered comparative clinical effectiveness research: Patient-Centered Outcomes Research Institute’s unique contribution to research done differently (2025) · cites this paper as [6]
The Discussion addresses transparency about where and how AI was used in a publication, not the interpretability of generative models. Although its Limitations section notes that LLaMA is open source, our copy does not say that other models’ technical details, weights, or datasets are proprietary and undisclosed. Searches for those terms and for model opacity found no supporting passage. (LLM, conf=0.73)
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Zheng, X., Lai, Y., Yi, J., Li, Z., Zhao, J., & Hu, L. (2026) · BMC Gastroenterology · doi:10.1186/s12876-026-04827-0
Artificial Intelligence for Discovery in Life Sciences
Chanda, S., Rizzoli, S. O., & Shaib, A. (2026) · Bioconjugate Chemistry · doi:10.1021/acs.bioconjchem.6c00230
Komut, E., Günay, S., Öztürk, A., Yanarates, G., Karacif, O., & Komut, S. (2026) · BMC Medical Imaging · doi:10.1186/s12880-026-02618-3
Navigating academic integrity in the Era of generative AI: a systematic literature review
Hendal, B. A., & Chowdhury, G. G. (2026) · AI and Ethics · doi:10.1007/s43681-026-01431-6 · Cited by 1 · see citing works
Baran, M. F., & İlhan, Ö. A. (2026) · Healthcare · doi:10.3390/healthcare14213611
- Authors
- Barrington, N. M., Gupta, N., Musmar, B., Doyle, D., Panico, N., Godbole, N., Reardon, T., & D’Amico, R. S.
- Affiliations
- Rosalind Franklin University of Medicine and Science (US)
- Campbell University (US)
- An-Najah National University (PS)
- Central Michigan University (US)
- Lake Erie College of Osteopathic Medicine (US)
- Tulane University (US)
- Henry Ford Health (US)
- Lenox Hill Hospital (US)
- Corresponding author
- Barrington, Nikki M.
- ORCID
- Venue
- Medical Sciences
- Publisher
- MDPI AG
- Published
- 2023-09-17
- Volume
- 11
- Issue
- 3
- Pages
- 61
- ISSN
- 2076-3271 (ISSN-L)
- PMC
- PMC10535733
- OpenAlex
- W4386837623
- Cited by
- 69
- References
- 37
- Citations by year
- 2026: 8 · 2025: 29 · 2024: 30 · 2023: 2
- FWCI
- 0.81
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Medical Sciences (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › scientometrics and bibliometrics research
- Social Sciences › Arts and Humanities › History and Philosophy of Science › Academic Writing and Publishing
- Keywords
- ChatGPT, large language models, artificial intelligence in healthcare, medical research, publication trends, citation analysis, medical education, patient counseling, radiology, surgical specialties, AI adoption policy
- MeSH terms
- Artificial Intelligence, Humans, Radiology, Bibliometrics, Benchmarking, Biomedical Research
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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