Japanese journal of radiology · 2026
Quality and reliability of YouTube videos on PET/CT radiation safety: a comparative analysis between human experts and large language models.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Shih Pei-Fan
Аннотация
PURPOSE: To 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.
METHODS: A 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).
RESULTS: The 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.
CONCLUSION: YouTube 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.
METHODS: A 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).
RESULTS: The 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.
CONCLUSION: YouTube 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.