Agenda

Artificial intelligence is reshaping how health systems detect disease, deliver care, and manage resources. Yet the promise of AI in health remains unevenly distributed, with innovation concentrated in high-income settings while the countries that stand to benefit most are often left as recipients rather than contributors. Furthermore, the lack of sufficient data that adequately represents the global population diversity in terms of traditional lifestyles, cultures, ethnicities, languages, and other relevant parameters greatly contributes to the varying degrees of effectiveness of data-driven AI solutions.

Egypt's AI SHARE initiative offers a different model. Built on locally developed AI diagnostic tools trained on a diverse set of Egyptian data, and designed from the outset to be shared with neighboring Arab and African countries, AI SHARE embodies a South-to-South vision of AI cooperation, one where countries in the Global South develop, own, and transfer solutions to one another rather than wait for packaged technology to trickle down from remote places and dissimilar environments.

This session will examine what it takes to build that kind of flexible models at scale. It will explore the conditions needed to move AI health innovations from national impact to regional relevance, and what governance, data, and institutional frameworks are required to make that journey responsible and sustainable.

The panel discussion could be structured around three guiding questions:

• What institutional and regulatory frameworks are needed to ensure AI health solutions can be responsibly adapted and deployed across different national contexts? 
• What data governance and interoperability standards must be established before AI in health can scale effectively across borders? 
• How can South-to-South cooperation models reshape the global AI health landscape, and what investment and partnership structures are needed to sustain them?