The World Health Organization has introduced a practical framework called RISE to help countries adopt artificial intelligence in healthcare safely and equitably, recognizing that AI governance must adapt to each nation's unique health system and resources. Rather than imposing rigid global rules, the Global Initiative on Artificial Intelligence for Health (GI-AI4H), a collaboration between the WHO, International Telecommunication Union, and World Intellectual Property Organization, offers a modular approach that countries can customize based on their existing capacity and regulatory maturity.

Why Is Localized AI Governance Becoming Critical in Healthcare?

The rapid integration of AI tools into healthcare systems worldwide has created a governance gap. While countries like the European Union have established risk-based regulatory frameworks through the EU AI Act, and nations such as India, Saudi Arabia, and Zambia have introduced data protection laws, many healthcare systems lack standardized approaches to ensure AI is deployed safely and fairly. This patchwork of regulations creates risks: AI systems trained on data from wealthy nations may perform poorly for patients in lower-income countries, and without proper oversight, existing health inequalities can widen rather than shrink.

The GI-AI4H addresses this challenge by acknowledging a fundamental truth that decades of implementation research has revealed: having new technology is not enough. Countries need to understand why, how, and where AI should be used within their specific health systems. Success depends on local knowledge about community health needs, existing barriers to care, and the social factors that shape health outcomes.

What Is the RISE Framework and How Does It Work?

The RISE framework consists of four interconnected components designed to be flexible rather than sequential. Countries can prioritize and phase these elements based on their current situation:

• Robust Capacity Building: Training healthcare workers, researchers, and policymakers in digital skills and AI literacy so they can effectively adopt and oversee AI tools in their own contexts.
• Inclusive Research Agenda: Identifying health challenges specific to each region and ensuring that AI development addresses the priorities and populations that matter most locally, not just globally profitable applications.
• Smart and Accessible Infrastructure: Building governance systems and affordable technology solutions that work in low-resource settings, recognizing that many countries lack the computing power or regulatory infrastructure of wealthy nations.
• Equitable and Secure Data Practices: Establishing fair, transparent data collection methods so that AI models trained on diverse populations produce reliable results for everyone, not just privileged groups.

These components are designed to be mutually reinforcing and modular. A country with strong regulatory capacity but limited technical infrastructure might prioritize smart infrastructure first, while another nation with active research institutions but weak governance might begin with capacity building and inclusive research.

How Are Countries Already Implementing This Approach?

Recent consultations with multiple nations reveal distinct priorities. Saudi Arabia, for example, has invested in health sector transformation programs that use AI to optimize hospital operations and expand virtual care services, while simultaneously building local capacity through training initiatives for researchers and practitioners. These efforts demonstrate that capacity building must align with existing national regulatory frameworks, ensuring that AI adoption strengthens rather than bypasses local governance structures.

The framework recognizes that implementation is not a one-time event but an ongoing process shaped by local characteristics. What works in a well-resourced urban hospital may not translate to a rural clinic with limited electricity or internet connectivity. The RISE framework empowers local decision makers to tailor AI adoption to their unique circumstances rather than forcing them to adopt solutions designed elsewhere.

What Role Does Global Coordination Play?

While the RISE framework emphasizes local adaptation, it operates within a broader ecosystem of global coordination. The GI-AI4H brings together researchers, policymakers, donors, regulators, patients, and civil society organizations across public, private, and nonprofit sectors. This collaborative structure enables knowledge sharing and pooled investments while respecting the principle that implementation decisions must remain local.

The initiative rests on three foundational pillars: enabling robust governance standards and normative guidance; facilitating a global community of experts and resources; and implementing sustainable country-level programs. Together, these pillars create a system where global best practices inform local decisions without dictating them.

The GI-AI4H's launch represents a significant shift in how the global health community approaches AI governance. Rather than waiting for perfect regulations or assuming that solutions from wealthy nations will work everywhere, the framework acknowledges that responsible AI in healthcare requires deep understanding of local contexts, sustained investment in local capacity, and governance structures that communities themselves help design and implement.