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What Drives Users' Intention to Discern AI-Generated Health Misinformation? Evidence from a Scenario-Based Experiment
Chenyu Gu1,2, Xiaojie Zhuo3, Kunling Jiang4
1Fujian Digital Media Economy Research Center, Fujian Social Science Research Base, Minjiang University, Fuzhou, China.
Journal of Health Communication
|June 6, 2026
Summary
People increasingly trust AI health information, leading to misinformation. This study shows AI diagnostic capability reduces discernment, especially for older adults, highlighting the need for critical evaluation of AI-generated health content.
Area of Science:
- Health Communication
- Artificial Intelligence Ethics
- Information Science
Background:
- Generative AI (GenAI) is a primary source of health information, leading to uncritical acceptance and an AI-driven infodemic.
- Automation bias in the AI era necessitates proactive user discernment to maintain cognitive sovereignty and governance.
- Understanding user intention to discern AI-generated health misinformation is crucial for effective communication strategies.
Purpose of the Study:
- To investigate the mechanisms and factors influencing users' intention to discern AI-generated health misinformation.
- To examine the role of perceived diagnostic capability and AI cognitive tendency based on the Elaboration Likelihood Model (ELM).
- To identify key information factors affecting discernment intention.
Main Methods:
- Scenario-based experiment with 594 participants.
- Structural Equation Modeling (SEM) and Artificial Neural Networks (ANN) were employed for analysis.
- Investigated user intention to discern AI-generated health misinformation.
Main Results:
- Perceived AI diagnostic capability reduces discernment intention by lowering uncertainty, moderated by AI cognitive tendency.
- Older users exhibit lower discernment intention.
- Information quality and explainability significantly impact perceived diagnostic capability; transparency and accountability show nonlinear effects, while fairness does not.
Conclusions:
- AI's perceived diagnostic capability can decrease user vigilance against health misinformation.
- User demographics (age) and AI characteristics (cognitive tendency) influence discernment.
- Information quality, explainability, transparency, and accountability are key factors in mitigating AI-driven health misinformation.
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