ArticleJapanese journal of radiology2026
Quality and reliability of YouTube videos on PET/CT radiation safety: a comparative analysis between human experts and large language models.
Article in Japanese journal of radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeTo evaluate the quality and accuracy of YouTube videos regarding PET/CT radiation safety and to assess the feasibility of using a Large Language Model (LLM) as an automated tool for content moderation compared to a human expert.
methodsA systematic search was conducted on YouTube using keywords related to "PET/CT radiation safety." A total of 42 videos were included and categorized by uploader source (Professional vs. Non-Professional). Video quality was assessed using the modified DISCERN (mDISCERN) tool and the Global Quality Scale (GQS). Usefulness and engagement metrics (views, likes) were analyzed. Additionally, inter-rater reliability between a human physician with clinical work experience in a nuclear medicine department and an AI model (Gemini-3) was evaluated using Cohen's Kappa (κ).
resultsThe majority of content (90.5%) originated from professional sources. Professional videos demonstrated significantly higher information quality compared to non-professional videos (mean mDISCERN: 3.71 vs. 2.50, P = 0.032; mean GQS: 4.18 vs. 2.50, P = 0.005). However, video popularity was not an indicator of quality, as no significant correlation was found between view counts and mDISCERN scores (ρ = 0.124, P = 0.432). Notably, the agreement between the human expert and the AI model was only slight (κ = 0.191). Discrepancy analysis revealed that the AI model systematically overestimated the quality of content containing commercial bias while penalizing highly technical academic videos for their presentation style.
conclusionYouTube contains high-quality information on PET/CT safety, predominantly from professional sources; however, the platform's algorithm does not prioritize clinical accuracy, often favoring lower-quality content. Furthermore, current LLMs lack the contextual judgment required to reliably detect commercial bias and misinformation, underscoring the irreplaceable role of physician oversight in digital patient education.
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