ArticleJournal of CME2024
Revolutionising Faculty Development and Continuing Medical Education Through AI-Generated Videos.
Article in Journal of CME, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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
5 citing papers in PubMed.
- Talking surveys: How photorealistic embodied conversational agents shape response quality, engagement, and satisfaction.Behavior research methods · 2026Article
- Applications of Generative Artificial Intelligence for Strabismus Surgery Video-Based Education.Ophthalmology science · 2026Article
- AI-generated videos in medical science popularization and education: a scoping review.Frontiers in public health · 2026Article
- Review
- Artificial Intelligence Generated Videos as Supportive Tools in Medical Education: A Scoping Review.Journal of medical education and curricular developmentReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Producing high-quality and engaging educational videos for continuing medical education (CME) is traditionally time-consuming and costly. Generative AI tools have shown promise in creating synthetic videos that mimic traditional lecture videos. We conducted a comparative analysis of four AI video generation platforms HeyGen, Synthesia, Colossyan, and HourOne using the Kano model. Our analysis revealed that HeyGen met most of our requirements. We created two videos and collected feedback from 25 learners. The feedback indicated that the videos were of good quality, engaging, and well-paced for learning. Only 32% recognised the videos as AI-generated, citing limited facial expressions, hand gestures and monotone vocal expression. Importantly, only 24% considered disclosure of AI-generated content necessary. This research indicates that AI-generated videos can be a viable alternative to traditionally produced educational videos. It offers an efficient, cost-effective solution for producing educational content. Ethical considerations regarding AI content disclosure should be addressed to maintain transparency.
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.