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Generative artificial intelligence in public health: a framework for governance and systemic integration.
Ruiye Yang1, Mengqi Deng1, Xiaoran Zheng2
1Department of Gynecological Oncology, Beijing Obstetrics and Gynecology Hospital, Beijing Maternal and Child Health Care Hospital, Capital Medical University, Beijing, China.
Frontiers in Medicine
|June 29, 2026
Summary
Generative artificial intelligence (GenAI) can enhance public health but requires mature systems for integration. Responsible innovation necessitates governance focusing on transparency, accountability, and equity for trustworthy AI in health.
Area of Science:
- Public Health
- Artificial Intelligence
- Health Systems Science
Background:
- Generative artificial intelligence (GenAI) offers transformative potential for public health via data synthesis and predictive modeling.
- The integration of advanced AI into public health systems is a critical area of research.
Purpose of the Study:
- To systematically review the interplay between GenAI advancements and public health system adaptation.
- To identify constraints and propose governance strategies for responsible GenAI integration in public health.
Main Methods:
- Systematic review of 119 key studies.
- Analysis using a co-evolutionary lens and the theory of Responsible Innovation.
Main Results:
- Effective GenAI integration is constrained by system maturity (infrastructural, institutional, human resources).
- Key governance challenges lie in technical transparency, institutional accountability, and ethical equity.
- A three-layer governance framework is proposed to address these challenges.
Conclusions:
- Trustworthy AI ecosystems in public health require more than technical capabilities.
- Institutional foresight, inclusive governance, and a commitment to equity are essential for human-centered health futures.
- GenAI integration necessitates a holistic approach considering system readiness and ethical implications.
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