Evolution of Artificial Intelligence in Medical Education From 2000 to 2024: Bibliometric Analysis
BackgroundIncorporating artificial intelligence (AI) into medical education has gained significant attention for its potential to enhance teaching and learning outcomes. However, it lacks a comprehensive study depicting the academic performance and status of AI in the medical...
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JMIR Publications
2025-01-01
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| Series: | Interactive Journal of Medical Research |
| Online Access: | https://www.i-jmr.org/2025/1/e63775 |
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| author | Rui Li Tong Wu |
| author_facet | Rui Li Tong Wu |
| author_sort | Rui Li |
| collection | DOAJ |
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BackgroundIncorporating artificial intelligence (AI) into medical education has gained significant attention for its potential to enhance teaching and learning outcomes. However, it lacks a comprehensive study depicting the academic performance and status of AI in the medical education domain.
ObjectiveThis study aims to analyze the social patterns, productive contributors, knowledge structure, and clusters since the 21st century.
MethodsDocuments were retrieved from the Web of Science Core Collection database from 2000 to 2024. VOSviewer, Incites, and Citespace were used to analyze the bibliometric metrics, which were categorized by country, institution, authors, journals, and keywords. The variables analyzed encompassed counts, citations, H-index, impact factor, and collaboration metrics.
ResultsAltogether, 7534 publications were initially retrieved and 2775 were included for analysis. The annual count and citation of papers exhibited exponential trends since 2018. The United States emerged as the lead contributor due to its high productivity and recognition levels. Stanford University, Johns Hopkins University, National University of Singapore, Mayo Clinic, University of Arizona, and University of Toronto were representative institutions in their respective fields. Cureus, JMIR Medical Education, Medical Teacher, and BMC Medical Education ranked as the top four most productive journals. The resulting heat map highlighted several high-frequency keywords, including performance, education, AI, and model. The citation burst time of terms revealed that AI technologies shifted from imaging processing (2000), augmented reality (2013), and virtual reality (2016) to decision-making (2020) and model (2021). Keywords such as mortality and robotic surgery persisted into 2023, suggesting the ongoing recognition and interest in these areas.
ConclusionsThis study provides valuable insights and guidance for researchers who are interested in educational technology, as well as recommendations for pioneering institutions and journal submissions. Along with the rapid growth of AI, medical education is expected to gain much more benefits. |
| format | Article |
| id | doaj-art-04caf0fb7e05459e8502008985809400 |
| institution | Kabale University |
| issn | 1929-073X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Interactive Journal of Medical Research |
| spelling | doaj-art-04caf0fb7e05459e85020089858094002025-01-30T18:00:51ZengJMIR PublicationsInteractive Journal of Medical Research1929-073X2025-01-0114e6377510.2196/63775Evolution of Artificial Intelligence in Medical Education From 2000 to 2024: Bibliometric AnalysisRui Lihttps://orcid.org/0009-0009-5121-9815Tong Wuhttps://orcid.org/0000-0002-0842-5623 BackgroundIncorporating artificial intelligence (AI) into medical education has gained significant attention for its potential to enhance teaching and learning outcomes. However, it lacks a comprehensive study depicting the academic performance and status of AI in the medical education domain. ObjectiveThis study aims to analyze the social patterns, productive contributors, knowledge structure, and clusters since the 21st century. MethodsDocuments were retrieved from the Web of Science Core Collection database from 2000 to 2024. VOSviewer, Incites, and Citespace were used to analyze the bibliometric metrics, which were categorized by country, institution, authors, journals, and keywords. The variables analyzed encompassed counts, citations, H-index, impact factor, and collaboration metrics. ResultsAltogether, 7534 publications were initially retrieved and 2775 were included for analysis. The annual count and citation of papers exhibited exponential trends since 2018. The United States emerged as the lead contributor due to its high productivity and recognition levels. Stanford University, Johns Hopkins University, National University of Singapore, Mayo Clinic, University of Arizona, and University of Toronto were representative institutions in their respective fields. Cureus, JMIR Medical Education, Medical Teacher, and BMC Medical Education ranked as the top four most productive journals. The resulting heat map highlighted several high-frequency keywords, including performance, education, AI, and model. The citation burst time of terms revealed that AI technologies shifted from imaging processing (2000), augmented reality (2013), and virtual reality (2016) to decision-making (2020) and model (2021). Keywords such as mortality and robotic surgery persisted into 2023, suggesting the ongoing recognition and interest in these areas. ConclusionsThis study provides valuable insights and guidance for researchers who are interested in educational technology, as well as recommendations for pioneering institutions and journal submissions. Along with the rapid growth of AI, medical education is expected to gain much more benefits.https://www.i-jmr.org/2025/1/e63775 |
| spellingShingle | Rui Li Tong Wu Evolution of Artificial Intelligence in Medical Education From 2000 to 2024: Bibliometric Analysis Interactive Journal of Medical Research |
| title | Evolution of Artificial Intelligence in Medical Education From 2000 to 2024: Bibliometric Analysis |
| title_full | Evolution of Artificial Intelligence in Medical Education From 2000 to 2024: Bibliometric Analysis |
| title_fullStr | Evolution of Artificial Intelligence in Medical Education From 2000 to 2024: Bibliometric Analysis |
| title_full_unstemmed | Evolution of Artificial Intelligence in Medical Education From 2000 to 2024: Bibliometric Analysis |
| title_short | Evolution of Artificial Intelligence in Medical Education From 2000 to 2024: Bibliometric Analysis |
| title_sort | evolution of artificial intelligence in medical education from 2000 to 2024 bibliometric analysis |
| url | https://www.i-jmr.org/2025/1/e63775 |
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