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Public Health Responsible AI Capability (PH-RAIC) Framework: A Conceptual Model for Integrating AI into Public Health
Arnob Zahid1, Ravishankar Sharma2, Rezwan Ahmed3
1Waikato Management School, University of Waikato, Hamilton 3240, New Zealand.
Healthcare (Basel, Switzerland)
|May 27, 2026
Summary
A new framework, the Public Health Responsible AI Capability (PH-RAIC), translates artificial intelligence (AI) principles into actionable practices for public health agencies, enhancing responsible AI implementation in public health.
Area of Science:
- Public Health Informatics
- Artificial Intelligence Governance
- Health Systems Strengthening
Background:
- Artificial intelligence (AI) is increasingly integrated into core public health functions, including disease surveillance and resource planning.
- Fragmented global health data systems and non-interoperable digital platforms can exacerbate existing inequities.
- Effective governance is crucial to balance AI innovation with the protection of rights, safety, and public trust in health data.
Purpose of the Study:
- To develop a conceptual meso-level capability framework for responsible AI implementation in public health agencies.
- To translate abstract responsible AI principles into practical organizational practices tailored for public health contexts.
Main Methods:
- A targeted narrative synthesis of current governance guidance and early AI implementation experiences in public health.
- A structured expert panel consultation with 9 experts to validate the proposed framework's face and content validity.
- Purposive selection of implementation experiences representing diverse current practices and debates in AI in public health.
Main Results:
- Introduction of the Public Health Responsible AI Capability (PH-RAIC) framework, adapting AI principles (transparency, accountability, fairness, ethics, safety) to public health realities.
- Identification of four interdependent capability domains within PH-RAIC: strategic governance, data stewardship, participatory design, and lifecycle oversight.
- Expert panel validation confirmed strong content validity for all four domains, with Content Validity Index (CVI) values ≥ 0.85.
Conclusions:
- The PH-RAIC framework provides practical tools, including practices, diagnostic questions, and indicators, to guide public health agencies in AI adoption.
- It supports navigating efficiency-equity trade-offs, thereby strengthening legitimacy and accountability in AI-driven public health systems.
- PH-RAIC offers a validated conceptual foundation for future empirical research and the development of operational AI readiness tools for public health.
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