Algorithmic Health-Information Exposure and Credibility Calibration Under AI-Generated Content Uncertainty
Main Article Content
Abstract
The rapid integration of algorithmic recommendation systems and generative artificial intelligence into health information dissemination has fundamentally transformed how individuals access and evaluate medical knowledge. This paper investigates the complex dynamics of algorithmic health-information exposure and the subsequent processes of credibility calibration under conditions characterized by artificial intelligence generated content uncertainty. As users increasingly rely on digital platforms for health guidance, algorithms dictate the visibility of information based on engagement metrics rather than medical accuracy, creating vulnerabilities to misinformation. The emergence of sophisticated generative models exacerbates this issue by producing highly plausible but potentially inaccurate health advice, introducing a novel layer of uncertainty. Through a comprehensive academic lens, this study explores how exposure algorithms interact with user cognition, examining the mechanisms through which individuals calibrate the credibility of health information when the source material may be artificially generated. The research delineates the pathways of information exposure, assesses the factors influencing user trust, and proposes an analytical framework for understanding the divergence between perceived and actual information credibility. By systematically analyzing the intersection of recommendation algorithms, generative artificial intelligence, and health communication, this paper contributes to the broader discourse on digital health literacy and system design. The findings underscore the critical need for transparent algorithmic architectures and robust user calibration mechanisms to mitigate the risks associated with automated health content dissemination.
Downloads
Article Details
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
-
All published articles are open access, with copyright shared between the authors and the journal. The journal retains the exclusive right to first publication and identifies itself as original publisher.
-
Articles are made available under a Creative Commons CC BY‑NC‑ND 4.0 license, permitting users to copy and distribute the content for non‑commercial purposes only, without modification, provided proper credit is given to authors and JCPHS.
-
Redistribution or reuse must preserve the full citation details and include a link to the license. No derivatives, adaptations, or commercial uses are permitted without prior written permission.
References
1. Rui, L., & Jingzhu, W. (2020). Research on public willingness to use the national public culture cloud APP. Information and documentation services, 41(4), 39-48.
2. Zhou, Yuehong, et al. "Adaptive Behavior in Digital Nomads: An Interactive Spatial Structure Study Using Machine Learning."
3. Bower, J. L., & Christensen, C. M. (1995). Disruptive technologies: Catching the wave. Harvard Business Review, 73(1), 43–53.
4. Ou, M., Zheng, H., Zeng, Y., & Hansen, P. (2026). Trust it or not: Understanding users’ motivations and strategies for assessing the credibility of AI-generated information. New media & society, 28(2), 507-529.
5. Ho, S. S., Ou, M., & Ong, Z. T. (2023). Exploring the general public’s and experts’ risk and benefit perceptions of cultured meat in Singapore: A mental models approach. PLoS One, 18(11), e0295265.
6. Liu, R., McKay, D., & Buchanan, G. (2021). Humanities scholars and digital humanities projects: Practice barriers in tools usage. In International Conference on Theory and Practice of Digital Libraries (pp. 215-226). Springer, Cham.
7. Huang, Y.; He, W.; Zhao, M.; McClements, D.J.; Xu, Y.; Li, L.; Dong, W.; Hu, X.; Li, C. Unraveling the health contributions of five key bioactives in virgin olive oil: A dose-based comparative review. Trends Food Sci. Technol. 2025, 164, 105274.
8. Yu, X., Wei, Y., Zhou, S., Yang, Z., Sun, L., Peng, H., ... & Yu, P. S. (2025, April). Towards effective, efficient and unsupervised social event detection in the hyperbolic space. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 39, No. 12, pp. 13106-13114).
9. Wu, Y., Jiang, J., & Rahbarimanesh, A. (2026). Simulation-Based Teaching in Project Management Education: A Systematic Literature Review and Bibliometric Analysis. Available at SSRN 6334578.
10. Sha, L., Wang, Y., Meng, P., Deng, Y., Chen, T., Zhang, X., ... & Xu, Q. (2025). Pharmacological inhibition of PSPH reduces serine levels and epileptic seizures. Nature Chemical Biology, 21(11), 1742-1753.
11. Yang Z, Wei Y, Li H, et al. Adaptive differentially private structural entropy minimization for unsupervised social event detection[C]//Proceedings of the 33rd ACM international conference on information and knowledge management. 2024: 2950-2960.
12. Zhang, B., Wang, S., Jiang, Y., Sui, D., Tu, Z., & Chu, D. (2025, July). Ask and Retrieve Knowledge: Towards Proactive Asking with Imperfect Information in Medical Multi-turn Dialogues. In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1055-1065).
13. Han, K., Zhang, J., Miao, X., Yu, Y., Zhang, L., Zhao, J., ... & Wang, W. (2026). PSMA‐Triggered Phase Separation of Peptide‐Drug Conjugates Enables Zinc‐Dependent Tumor Inhibition. Angewandte Chemie International Edition, 65(2), e18980.
14. Meng, X., Zhang, Y., & Lin, J. (2026). PEARL: A Task-Aware Framework for Evaluating Differentially Private Synthetic Educational Data. arXiv preprint arXiv:2609.10612.
15. Mo, Z. (2025, May). Development of an Intelligent Retrieval and Recommendation System for Chinese Language Educational Resources Based on DNN Technology. In 2025 2nd International Conference on Intelligent Computing and Robotics (ICICR) (pp. 170-175). IEEE.
16. Pan, S. L., Li, K., Wang, D. H., Zhang, X. Y., Liu, J., & Zhu, S. (2025). Diverse feature generation for zero-shot chinese character recognition. Expert Systems with Applications, 129442.
17. Zhang, X. (2026, June). Workforce Reskilling and Digital Transformation: Empirical Analysis of Human Capital Agility in Global E-Commerce Platforms. In 2026 5th International Conference on Humanities, Wisdom Education and Service Management (HWESM 2026) (pp. 692-703). Atlantis Press.
18. Meng, X., & Lin, J. (2026). PRISM-Twin: Data-Minimizing and Interpretable Multimodal Campus IoT Student-State Modeling for Risk Support. IEEE Internet of Things Journal.
19. Ou, M., & Ho, S. S. (2024). Factors associated with information credibility perceptions: A meta-analysis. Journalism & Mass Communication Quarterly, 101(2), 346-372.
20. Zhou, Y. (2022). Pre-disaster relocation and agent-based model for flood disaster. The University of Wisconsin-Madison.
21. Ou, M., & Ho, S. S. (2022). A meta-analysis of factors related to health information seeking: An integration from six theoretical frameworks. Communication Research, 49(4), 567-593.
22. Chiu, P. W., Yip, H. C., Xia, X. F., Chan, S. M., Ng, E. K., & Lau, J. Y. (2019). How I do it: Flexible 3‐D endoscope for endoscopic submucosal dissection. Digestive Endoscopy, 31(3), 323-328.
23. Bian, X., & Brown, A. (2023). Establishing the potential for the application of digital storytelling to support Chinese industrial heritage. Acta Horticulturae et Regiotecturae, 26(2), 147-156.
24. Zhao, J., Zhang, C., Qin, M., & Yang, P. (2025). QuantFactor REINFORCE: mining steady formulaic alpha factors with variance-bounded REINFORCE. IEEE Transactions on Signal Processing, 73, 2448-2463.
25. He, H., Sun, Z., Chen, X., Tao, X., Tao, M., Dong, D., ... & Qu, C. (2025). Exposure to volatile organic compounds and suicidal ideation: Insights from a US population-based study. Journal of Affective Disorders, 379, 194-203.
26. Liu, X., Yu, Z., Yao, Y., Yang, K., Zeng, H., & Chen, C. P. (2026). A Comprehensive Survey of Cross-Modal Retrieval: Breaking Through Modal Barriers. ACM Computing Surveys.