ReviewInternational journal of emergency medicine2026
Ethical and responsible use of artificial intelligence in medical education.
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 (AI) has rapidly transformed medical education through virtual patient simulations, adaptive learning systems, and large language models like ChatGPT, while raising concerns about bias, transparency, reproducibility, and ethical integration into training and assessment. Despite its growing relevance, the ethical and responsible use of AI in medical education remains understudied. This bibliometric analysis aims to characterize the intellectual landscape of this field.
methodsBibliographic data were obtained from the Web of Science Core Collection using a search strategy combining medical education and AI-related terms, limited to publications from 1995 to 2026. A total of 1403 articles were retrieved. Microsoft Excel was used to generate descriptive figures, and VOSviewer (version 1.6.20) was used to construct bibliometric networks of country, institutional, and keyword co-occurrence relationships.
resultsPublication output rose sharply after 2023, from 113 publications to 500 by 2025, coinciding with the rise of ChatGPT and other large language models. The United States led in publication volume (533) and citation influence, followed by China (189) and England (95). Harvard University and its affiliated entities led institutional output, while Harvard Medical School, Stanford, and University of California San Francisco led citation strength. The most-cited publications centered on ChatGPT's performance on medical licensing examinations.
conclusionsThis field has grown rapidly but remains concentrated among few countries and institutions, with citation impact dominated by capability-testing studies rather than governance-focused scholarship. Future research should prioritize empirical evaluation of AI governance frameworks and broader institutional and geographic representation.
Indexed as
Identifiers
What OpenQuestion holds
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.