ArticleFrontiers in digital health2026
Artificial intelligence and infodemic management: a governance framework for digital public health decision-making.
Article in Frontiers in digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Digital communication ecosystems have profoundly transformed the circulation of health information. During public health crises, large volumes of information-including misinformation and disinformation-can spread rapidly across online platforms, generating complex information environments commonly described as infodemics. These dynamics can influence public risk perception, undermine trust in health institutions, and affect adherence to preventive measures. At the same time, advances in artificial intelligence (AI) and digital technologies have created new opportunities for monitoring information flows and identifying emerging misinformation patterns in real time. However, the translation of AI-generated insights into coordinated public health decision-making processes remains limited. Methods: This study developed a conceptual governance framework aimed at explaining how AI-generated insights derived from digital information ecosystems can be translated into evidence-informed public health responses. The framework was developed through a secondary conceptual analysis of the complete evidence extraction database generated from a previously published scoping review including 63 studies. The methodological approach combined thematic synthesis of empirical evidence with conceptual modeling techniques commonly used in public health systems research. Results: The synthesis of the literature identified several thematic domains describing how digital technologies and artificial intelligence are used to monitor and respond to health misinformation within digital communication ecosystems. Building on these domains, this study proposes a governance-oriented conceptual framework integrating digital data ecosystems, artificial intelligence analytics, interpretive public health expertise, and institutional governance mechanisms. Conclusions: Artificial intelligence technologies offer powerful tools for monitoring complex digital information environments, yet their integration into public health decision-making requires governance models capable of linking analytical insights with institutional responses. The proposed framework contributes to the emerging field of digital public health governance by providing a structured conceptual model for translating AI-generated insights into coordinated public health actions during infodemic events. Although the framework remains preliminary and requires formal validation, it provides a structured conceptual basis for future empirical implementation and governance research.
Indexed as
Identifiers
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.