BackgroundThe proliferation of short video platforms has transformed public health communication, yet the quality of medical information shared on these platforms remains inconsistent. Osteoarthritis (OA), a prevalent and burdensome chronic condition, is frequently featured in online health content. However, the reliability of such information has not been systematically evaluated across major Chinese short video platforms. To assess and compare the quality and reliability of OA-related health information on TikTok and Bilibili, and to examine the influence of uploader type and user engagement metrics on content quality.MethodsIn this cross-sectional study, a total of 189 OA-related videos were collected from TikTok (n = 96) and Bilibili (n = 93) using a standardized search strategy. Four validated instruments—the Journal of the American Medical Association (JAMA) benchmarks, modified DISCERN (mDISCERN), Global Quality Score (GQS), and Health on the Net Code (HONcode)—were used for video assessment. Each video was independently rated by two trained reviewers. Differences in quality scores were compared across platforms and uploader types (health professionals vs. non-professionals). Spearman correlation analysis was conducted to explore associations between video quality and engagement metrics (likes, comments, shares, favorites).ResultsTikTok videos exhibited significantly higher median scores on JAMA (2.4 vs. 2.1, P = 0.001), GQS (3.0 vs. 3.0, P = 0.006), and HONcode (11.0 vs. 9.3, P = 0.005) compared to Bilibili. No significant difference was observed for mDISCERN scores. Videos uploaded by healthcare professionals had significantly higher GQS (P = 0.004) and HONcode scores (P = 0.010) than those from non-professionals. User engagement metrics were positively correlated with content quality, particularly on TikTok (e.g., likes vs. JAMA, r = 0.732, P < 0.001).ConclusionsOA-related videos on TikTok demonstrate higher overall quality and reliability compared to Bilibili, especially when created by healthcare professionals. User engagement metrics are positively associated with information quality, underscoring the importance of expert-led digital health communication. These findings highlight the need for platform-level interventions to promote trustworthy content and improve the digital health information ecosystem.
A generalizable 3D framework and model for self-supervised learning in medical imaging
npj Digital Medicine, Published online: 07 November 2025; doi:10.1038/s41746-025-02035-w A generalizable 3D framework and model for self-supervised learning in medical imaging



