Artificial Intelligence Generated Videos as Supportive Tools in Medical Education: A Scoping Review
Pinto Francisco Impito
阅读操作
确认中在文库中上传 PDF 后可生成中文音频讲解。
摘要与影响
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.
逐年被引趋势
暂无年度引用数据
关键指标
同类平均 = 1
同领域 · 同年份 · 同类型
Google Scholar 与 OpenAlex 的被引统计范围不同,数值存在差异属正常。
AI 辅助阅读
依据:摘要
可就本文提问;依据不足时会说明。
学术脉络
学科主题
生物医学Artificial Intelligence in Healthcare and Education
Innovations in Medical Education · Simulation-Based Education in Healthcare
参考文献 51
此处列出前 3 条