ReviewInternational journal of emergency medicine2026
Charting the rise of artificial intelligence in medical education: a global bibliometric and network analysis, 2000-2026.
Review in International journal of emergency medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
backgroundArtificial intelligence has moved from a peripheral curiosity to a core feature of modern medical education through adaptive learning, simulation-based training, automated assessment, and large language models such as ChatGPT. Publication activity has accelerated rapidly, but the structure of this expanding literature has not been comprehensively mapped.
methodsBibliographic data were obtained from the Web of Science Core Collection using a search strategy that combined medical education terms in the title field with artificial intelligence terms in the topic field. Articles, review articles, and data papers published from 2000 to 2026 were included. A total of 2,298 records were retrieved. Microsoft Excel was used for descriptive analyses, and VOSviewer (version 1.6.20) was used to construct keyword co-occurrence, country collaboration, and institutional collaboration networks. Data for 2026 represent a partial calendar year.
resultsPublication output remained limited until the late 2010s and rose sharply after 2021. Of the 2,298 records, 2,066 (89.9%) were published from 2021 to 2026, with the highest complete-year output in 2025 (821 publications). The United States led in publication volume (918 records) and raw citation count (14,582 citations), followed by the People's Republic of China (367 records; 4,404 citations). The Journal of Surgical Education published the most articles. Harvard Medical School led institutional output, whereas Stanford University and the University of California San Francisco had higher raw citation totals despite fewer publications. Artificial intelligence, medical education, ChatGPT, medical students, and machine learning were the most prominent keywords.
conclusionsArtificial intelligence in medical education is a rapidly expanding but geographically and institutionally concentrated field. Raw citation patterns differed from publication volume, although these unadjusted measures should not be interpreted as direct comparisons of research quality. Thematic patterns emphasize learner-facing applications, assessment, and generative AI, while responsible curricular integration and empirical evaluation remain important priorities. These findings are especially relevant to emergency medicine education, where simulation, time-sensitive decision-making, and competency assessment are central.
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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.