Multi-layered Epistemic Disruption in AI-Driven Health Misinformation: Conceptual Framework and Viewpoint

Area of Science:

• Digital Health Communication
• Health Misinformation Studies
• Artificial Intelligence Ethics

Background:

• Generative AI (GenAI) enables scalable production of sophisticated health misinformation, challenging existing threat-detection frameworks.
• Current approaches overlook how AI embeds false claims across interconnected human trust systems.
• The health information ecosystem is increasingly vulnerable to AI-driven disinformation campaigns.

Purpose of the Study:

• Introduce the Multi-layered Epistemic Disruption Framework (MEDF) to conceptualize AI's structural impact on public trust.
• Detail four interdependent layers of disruption: discursive, biometric, temporal, and systemic.
• Provide a novel lens for understanding and addressing AI-driven health misinformation.

Main Methods:

• Adopt a socioecological and structural epistemic approach.
• Synthesize empirical findings from communication psychology, medical sociology, and digital infodemiology.
• Position the MEDF relative to existing health communication and infodemic models.

Main Results:

• AI-driven health misinformation exploits individual receptivity to authority and lowers epistemic thresholds across trust layers.
• Exposure to health misinformation correlates with increased distrust in healthcare institutions (OR 1.66).
• Existing defenses inadequately address temporal and systemic disruption layers, with limited effectiveness in real-world conditions.

Conclusions:

• Effective strategies require moving beyond individual-level interventions to structural, policy-driven responses targeting each MEDF layer.
• Implement source verification, biometric protection, ecosystem governance, and trust infrastructure, especially in LMIC contexts.
• Urgent policy calibration is needed to mitigate the cascading effects of AI-driven health misinformation.