Generative artificial intelligence in public health: a framework for governance and systemic integration
Year: 2026
Type: article
Study design: Systematic Review
Abstract: 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.
Source: Frontiers in Medicine
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W7164339688