From Apomediation to AImediation: Generative AI and the Reconfiguration of Informational Authority in Health Communication

Abstract

Objective: This conceptual paper explores the transition from apomediation to AIMediation, allowing patients or users to independently seek and access health information on their own, often using the internet and social networks, rather than relying exclusively on the intermediation of health professionals. It examines how generative artificial intelligence (GAI) reconfigures the dynamics of informational authority, access, and user autonomy in digital health environments in light of the increasing use of generative AI tools in healthcare contexts. Method: This study examined how mediation models in health information have changed over time. It uses Eysenbach’s framework and new developments in large language models (LLMs). A new model was created to compare intermediation, apomediation, and AImediation. Results: AImediation emerges as a new paradigm in which patients or users interact directly with AI tools such as ChatGPT, Claude, Perplexity, or Gemini to access compiled multi-source health information. While this model retains the user autonomy characteristic of apomediation, it centralizes information flows and removes peer-based social layers. Key challenges include algorithmic opacity, prompt dependence, and the risk of misinformation due to hallucinations or biased outputs. Conclusion: AImediation redefines how individuals access and evaluate health information, requiring critical engagement from users and responsible development by technology providers. This framework calls for more research to determine how it affects patient actions, the roles of professionals, and the ethical use of AI in healthcare.