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Review
. 2023 Sep 17;11(3):61.
doi: 10.3390/medsci11030061.

A Bibliometric Analysis of the Rise of ChatGPT in Medical Research

Nikki M Barrington  1 , Nithin Gupta  2 , Basel Musmar  3 , David Doyle  4 , Nicholas Panico  5 , Nikhil Godbole  6 , Taylor Reardon  7 , Randy S D'Amico  8
Affiliations
Review

A Bibliometric Analysis of the Rise of ChatGPT in Medical Research

Nikki M Barrington et al. Med Sci (Basel). .

Abstract

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.

Keywords: ChatGPT; artificial intelligence; large language models; medicine; research.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
PRISMA diagram demonstrating screening strategy for included articles.
Figure 2
Figure 2
Number of publications on ChatGPT in the medical field per month since the beginning of 2023.
Figure 3
Figure 3
Geospatial map showing the location of senior authors publishing on ChatGPT in medicine.
Figure 4
Figure 4
Topics of ChatGPT publications.
Figure 5
Figure 5
Visual map of the top keywords used in included studies. Larger font indicates higher frequency of keyword usage.
See this image and copyright information in PMC

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