Generative artificial intelligence in public health: a framework for governance and systemic integration

Generative artificial intelligence (GenAI) is poised to transform public health systems through its capacity for data synthesis and predictive modeling. This systematic review, analyzing 119 key studies, adopts a co-evolutionary lens to examine the dynamic interplay between GenAI advancements and public health system adaptation. We demonstrate that the effective integration of GenAI is fundamentally constrained by a system's infrastructural, institutional, and human resource maturity. Our analysis, grounded in the theory of Responsible Innovation, identifies three interconnected governance domains-technical transparency, institutional accountability, and ethical equity-that frame the core challenges. We subsequently propose a three-layer governance framework to navigate these issues, emphasizing that trustworthy AI ecosystems require more than technical excellence; they demand institutional foresight, inclusive governance, and a steadfast commitment to equitable, human-centered health futures.