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Certificate peer learning programme for AI in global health and humanitarian response

AI4Health: Get the first AI framework for global health

GET THE FRAMEWORK

Artificial intelligence (IA) is reshaping global health. Are you ready?

As AI capabilities advance rapidly, health leaders need to prepare, learn, and adapt.

The AI4Health Framework equips you to harness AI’s potential while protecting what matters most—human experience, local leadership, and health equity.

Who should use this framework?

Health system leaders and managers: Get practical tools to evaluate AI opportunities, make informed adoption decisions, and lead transformation that strengthens rather than fragments your system.

Policy makers and regulators: Access templates for governance frameworks that balance innovation with protection, ensuring AI serves public health goals.

Programme implementers: Find guidance for integrating AI into existing workflows while maintaining quality and equity in service delivery.

Funders and partners: Evaluate AI investments through frameworks for sustainable impact and genuine capacity building.

Certificate peer learning programme for AI in global health and humanitarian response

Building on this framework, The Geneva Learning Foundation (TGLF) is launching a comprehensive peer learning certificate programme. This programme will support health leaders to:

  1. Develop customized AI strategies for their contexts.
  2. Create implementation plans with peer and expert feedback.
  3. Build governance frameworks for their organizations.
  4. Join a global community of emerging AI4Health leaders for ongoing support.

Download the framework now to get early access to the Certificate programme.

Listen to a 4 minute overview of the AI4Health framework

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Why this framework matters now

Artificial intelligence (AI) is going to transform global health. From diagnostic algorithms to supply chain optimization, from drug discovery to predictive analytics—the transformation is underway.

Yet most ministries of health, health organizations, and health professionals lack practical frameworks to:

  1. Evaluate which AI tools genuinely serve their mission.
  2. Build capacity without creating dependencies.
  3. Ensure equity in AI deployment.
  4. Govern AI use effectively.

The AI4Health Framework provides a structured approach to navigating the challenges of AI in global health.

Seven AI4Health Framework principles

  1. Maintain human agency in decision-making.

  2. Augment rather than replace human leadership.

  3. Enhance collective intelligence.

  4. Enable rapid learning and adaptation.

  5. Preserve context sensitivity.

  6. Facilitate knowledge flow across boundaries.

  7. Build sustainable, hybrid human-AI learning systems that drive change.

About The Geneva Learning Foundation

Transforming how we learn and solve global health challenges

The Geneva Learning Foundation (TGLF) is a Swiss non-profit organization that operates as a distributed global network, pioneering innovative approaches to peer learning and capacity building in global health. Since 2010, our networked approach has connected and supported health professionals from 137 countries to surface insights from local knowledge, recognize and strengthen local leadership, and accelerate systemic change.

The AI transformation in global health is already happening. TGLF is committed to ensuring this transformation strengthens health systems, empowers health workers, and serves communities equitably. Through our AI4Health Framework and Certificate Programme, we are building a global community of health leaders to navigate this transformation thoughtfully and effectively.

TGLF embodies a new model where expertise and leadership emerge from the network itself rather than from a central headquarters. 

Our commitment to responsible and ethical AI in global health

Human agency first: We believe AI is about the future of humanity. Health is key to this future.

Equity by design: AI risks deepening global health inequities. Our approach prioritizes access, affordability, and appropriateness for all settings—from urban hospitals to rural health posts.

Local leadership: Too often, technology is imposed rather than integrated. We ensure AI adoption strengthens rather than undermines local health system capacity and leadership.

Evidence-based innovation: We ground our work in real implementation experience, not speculation. Our frameworks emerge from what health professionals are actually doing with AI today.

Why TGLF for AI in global health

Unique vantage point: We see AI transformation from every angle—from ministry planning rooms to village health posts.

Proven methodology: Our peer learning approach creates exactly the kind of collaborative intelligence needed to navigate AI transformation.

Global reach, local depth: Our networks span continents while maintaining deep connections to local realities.

Commitment to equity: We ensure AI serves those who need it most, not just those who can afford it.

Swiss non-profit foundation governance: We bring the rigor, neutrality, and long-term perspective that complex global challenges demand.

Our unique position in AI for global health

  • Deep field connections: Our peer learning networks connect frontline health workers, district managers, national programme leaders, and global health experts. This gives us unparalleled insight into how AI is actually being deployed, what works, how, and why.
  • Proven methodology: Our peer learning approach—recognized internationally as an effective model for addressing complex health challenges—creates the ideal foundation for navigating AI transformation. We understand how to build sustainable capacity at scale while maintaining quality and local relevance.
  • Bridge between innovation and implementation: The Geneva Learning Foundation is not a traditional headquarters-based organization but a distributed network of capabilities connecting health workers, humanitarian responders, and local communities with governments and global institutions. We translate emerging AI capabilities into practical approaches that work in resource-constrained settings.