ArticleFrontiers in medicine2026
Artificial intelligence vs. human evaluation of anesthesia education videos: a comparative analysis using validated quality scales.
Article in Frontiers in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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Who cites it
2 citing papers in PubMed.
- Large Language Models in Perioperative Antithrombotic Management: A Blinded Scenario-Based Expert Evaluation.Diagnostics (Basel, Switzerland) · 2026Article
- AI-generated videos in medical science popularization and education: a scoping review.Frontiers in public health · 2026Article
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Authors and funding
2 authors.
Funding
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Abstract
Background: YouTube has become an increasingly popular platform for medical education, yet the accuracy and educational quality of anesthesia-related videos remain uncertain. While human experts have traditionally assessed video quality using validated scales such as DISCERN, JAMA, and the Global Quality Scale (GQS), artificial intelligence (AI) models-particularly large language models (LLMs)-now offer new possibilities for scalable, objective content evaluation. This study aimed to compare the educational quality of anesthesia education videos produced by humans and AI, and to examine the level of agreement between human expert ratings and ChatGPT-5 evaluations. Methods: In this cross-sectional analytical study, forty YouTube videos were analyzed: 20 produced by human educators and 20 generated using AI tools. Each video was independently assessed by two anesthesiologists and by ChatGPT-5 Plus (OpenAI, 2025) using DISCERN, JAMA, and GQS criteria. Inter-rater reliability between human evaluators was determined using the Intraclass Correlation Coefficient (ICC), and correlations between human and AI ratings were analyzed with Spearman's rho. Results: Human-generated videos scored significantly higher than AI-generated ones in DISCERN (68.45 ± 4.60 vs. 62.77 ± 7.32, Conclusion: AI-based models such as ChatGPT-5 show potential to approximate human expert judgment in evaluating educational content. While human-generated videos remain superior in terms of source transparency and ethical reporting, AI-generated content approaches human quality in structural organization and linguistic fluency. These findings suggest that AI-assisted evaluation systems may serve as standardized, efficient tools for quality screening of large-scale educational video archives in medical education.
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Registered trials
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