To develop a conceptual governance framework that translates AI-generated insights from digital information ecosystems into evidence-informed public health responses.
Approach:
Framework Development: A conceptual governance framework was constructed through secondary conceptual analysis of an evidence-extraction database from a scoping review of 63 studies, integrating thematic synthesis with conceptual modeling techniques.
Key Findings:
The literature synthesis identified thematic domains for applying digital technologies and AI to monitor and address health misinformation.
The proposed governance framework integrates digital data ecosystems, AI analytics, public health expertise, and institutional governance mechanisms.
Interpretation:
AI technologies can effectively track complex digital information environments, but their integration into public health decision-making requires governance models that link analytical findings to institutional responses.
Limitations:
The framework is preliminary and requires formal validation.
Current institutional mechanisms for incorporating digital analytics into public health decision-making are underdeveloped.
Conclusion:
The framework provides a structured conceptual foundation for future empirical research and implementation in digital public health governance.
