Health Information Quality in the AI Era: Trust, Credibility, and Information Access Reshaping in the Global Communication Ecosystem

1. Executive Summary

1. Disruption of Information Access Mechanisms by AI: The AI information layer, through semantic understanding and knowledge representation, is reshaping the pathways through which the public accesses health information, shifting the communication system from "information dissemination" to "information understanding and utilization." Explanation: AI is no longer a simple conveyor of information but a new intermediary layer for structuring, retrieving, and interpreting information.
2. Structural Shift in Trust Sources: Faced with the proliferation of AI-generated content, public trust in human or professional sources still exhibits significant differences, signaling a shift in the "anchor" of trust from mere "authority" to "perceptibility." Explanation: Although AI content is rich, human studies show that in the health domain, human sources (like healthcare providers) still elicit stronger resonance in behavioral intention and concern.
3. Universal Metrics for Information Quality: Regardless of cultural or economic background, the core value of information quality remains focused on clarity, accessibility, and source identifiability, which are the foundations for cross-cultural information effectiveness. Explanation: These characteristics are prerequisites for effective information processing and the underlying constraints on content effectiveness in the AI era.
4. Adaptability Challenges for Communication Systems: Communication systems are undergoing a systemic transformation from centralized dissemination to diversified entry points, and finally to AI-assisted understanding. This poses structural demands on traditional information verification and distribution chains. Explanation: This shift requires communication practices to focus on matching content design with AI's understanding patterns, rather than solely focusing on content breadth.

2. Research Background

Why Study This Problem?

This study aims to explore how the cognitive structure regarding "information quality" in the health communication field evolves under the deep intervention of AI technology in the information ecosystem. With the explosion of digital media and AI-generated content, how does the public evaluate the "quality" of information—will these quality standards structurally change depending on the information source (human vs. AI)? The urgency of this research lies in the fact that unclear standards for evaluating information quality will lead to trust crises and biases in health behavior decisions.

Industry Background

Global health information is transitioning from being dominated by traditional media to a complex environment driven by multi-channel and AI. On one hand, the improvement of digital health literacy shows significant differences across countries with varying economic levels, directly affecting the quality and effectiveness of information reception (van Kessel et al., 2022). On the other hand, the popularization of AI-generated content has led to unprecedented increases in the "speed" and "breadth" of information access, but it simultaneously challenges the traditional perception that information quality is established by "institutional authority."### Technological Changes The core technological change lies in the rise of the AI Information Layer. AI no longer just provides content; it changes the way humans construct the "meaning" of information through mechanisms such as Retrieval, Entity Recognition, Semantic Understanding, and Knowledge Representation. This means that the standards for evaluating information quality must also take into account how "AI-generated content is structurally understood."