Artificial intelligence and infodemic management: a governance framework for digital public health decision-making

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.