ORIGINAL RESEARCH article
Front. Public Health
Sec. Public Health Education and Promotion
The Theoretical Impact of AI-Based Quality Evaluation of Short-Video Health Information on Public Cognition and Treatment Adherence: A Case Study of Denosumab Combined with PD-1/PD-L1 Therapy for Lung Cancer Bone Metastasis
Provisionally accepted
- The Fourth Hospital of Heibei Medical University, Shijiazhuang, China
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Background: Bone metastasis occurs in 30–40% of patients with advanced non-small cell lung cancer (NSCLC), and denosumab combined with PD-1/PD-L1 inhibitors has emerged as a promising treatment strategy. However, the "algorithmic echo chamber" effect on short-video platforms may distort patient cognition and treatment decision-making. Methods: A cross-sectional study was conducted using a custom-developed web crawler to collect 1,369 videos from Bilibili, Douyin, and Xiaohongshu. A total of 402 videos were included after a three-tier keyword filtering process. An AI-based evaluation system built upon the doubao-seed-1.6 model was established, integrating three international standards—Global Quality Score (GQS), Journal of the American Medical Association (JAMA) benchmark criteria, and the modified DISCERN tool—to assess multidimensional information quality. Kruskal–Wallis tests and Spearman correlation analyses were performed to explore inter-platform differences and the relationship between information quality and user engagement metrics. Results: Overall video quality was substantially below professional medical standards: the mean GQS was 2.84±1.06 (56.8% of the full score), JAMA was 0.34±0.57 (8.5%), and modified DISCERN was 1.55±0.69 (31.0%). Significant quality differences were observed across platforms (P<0.001, Cohen's d=0.6–0.8): Douyin ranked highest, followed by Xiaohongshu, with Bilibili lowest. Correlation between user engagement and content quality was extremely weak (R²=0.004, r=0.062), indicating substantial decoupling—high engagement did not equate to high-quality content. Medical professionals accounted for only 25.6% of content creators, while patient-generated content reached 52.2%. Evidence-based treatment information comprised merely 20.0–26.7%, whereas misleading or inaccurate claims accounted for 6.7–13.3%. Conclusion: From a behavioral and cognitive perspective, the low quality of immune-oncology information on short-video platforms, coupled with algorithm-driven amplification of high-engagement but low-quality content, may exacerbate cognitive bias, potentially increasing clinical safety risks such as insufficient hypocalcemia monitoring and inadequate MRONJ prevention. Establishing a professional governance and oversight system is urgently required.
Keywords: AI evaluation, algorithmic echo chamber, cognitive bias, Denosumab, Health information quality, immune checkpoint inhibitors, Lung cancer bone metastasis, short-videoplatforms
Received: 17 Oct 2025; Accepted: 03 Dec 2025.
Copyright: © 2025 Wang, Xun and Zhao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Fei-fei Zhao
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