Artificial Intelligence Generated Videos as Supportive Tools in Medical Education: A Scoping Review

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.

Publication Details

Journal
Journal of Medical Education and Curricular Development
Published
2026-01-01
DOI
https://doi.org/10.1177/23821205261437352
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
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article

Artificial Intelligence Generated Videos as Supportive Tools in Medical Education: A Scoping Review

Journal of Medical Education and Curricular Development
Artificial Intelligence in Healthcare and Education
article

Artificial Intelligence Generated Videos as Supportive Tools in Medical Education: A Scoping Review

article en

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.

Journal of Medical Education and Curricular DevelopmentVol. 13
Quality Education
Openalex Percentile: Top 12%
Artificial Intelligence in Healthcare and Education
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Artificial Intelligence Generated Videos as Supportive Tools in Medical Education: A Scoping Review · Journal of Medical Education and Curricular Development (2026) | TGRS Research Map | TGRS