ArticleJournal of primary care & community health
From Apomediation to AImediation: Generative AI and the Reconfiguration of Informational Authority in Health Communication.
Article in Journal of primary care & community health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- AI Health Literacy: a reflective framework for LLM-based generative AI in public health education and promotion.Frontiers in public health · 2026Article
- Media amplification, model source cues, and expectancy violation in public acceptance of generative AI: evidence from a health-consultation experiment.Frontiers in psychology · 2026Article
- Apomediation in the Age of TikTok: Reframing Health Literacy for Adolescents and Young Adults.Journal of primary care & community healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis 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.
methodThis 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.
resultsAImediation 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.
conclusionAImediation 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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.