ReviewJournal of medical education and curricular development
Artificial Intelligence Generated Videos as Supportive Tools in Medical Education: A Scoping Review.
Review in Journal of medical education and curricular development. 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
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Generative artificial intelligence (GenAI) has introduced a transformative approach in medical informatics and education. AI-driven video models, such as Sora, HeyGen, Synthesia, and Google Veo 3, among others, can autonomously generate realistic clinical materials, including synthetic patients and simulated scenarios. This technology system represents an emerging domain of medical learning informatics that integrates AI-generated content, simulation, and pedagogy. This scoping review, based on selected studies, identifies and synthesizes educational outcomes, highlights the methodological limitations of AI-generated videos as training tools for medical students, and explores technical and pedagogical challenges to guide future research. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews framework, the literature searches were conducted across Google Scholar, PubMed, ScienceDirect, Scopus, and gray literature sources. Studies were included if they focused on AI-generated videos as educational tools for medical education. A single reviewer conducted screening of titles, abstracts, and full texts, and data were systematically extracted using a standardized charting form, including study design, AI tool utilized, outcomes, limitations, and challenges. Of the 970 retrieved records, 8 studies met the inclusion criteria. The latter demonstrated that AI-generated videos can enhance knowledge retention, skill acquisition, and learner engagement, outperforming traditional methods of delivering practical exercises in medical education. Reported challenges included issues with accuracy, limited emotional authenticity, ethical standards, and the necessity for pedagogical consistency. AI-driven videos are a promising innovation in medical education, offering scalable, interactive, and personalized learning. However, their integration requires a solid validation framework, interdisciplinary collaboration, and governance models that guarantee ethical and pedagogically appropriate use. Additionally, long-term, cross-institutional studies are necessary to evaluate the lasting educational and clinical effects.
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.