As the world was still reeling from COVID-19, an outbreak of Mpox struck the globe from 2022 to 2023. The outbreaks were first seen in remote mining communities and last-mile villages in the Democratic Republic of Congo (DRC) and then spread to neighbouring countries. This resulted in a historically rare disease spreading widely outside its endemic regions to Europe and Asia.

Mpox is highly virulent and can be fatal. The regions where the disease is endemic see overlapping challenges of conflict, changing weather patterns and population displacement, intersecting with limited infrastructure and health personnel. In DRC and neighbouring regions that see continued outbreaks, rapid testing for Mpox within communities and at borders was and still is critical to maintain global health security.

This is where a new ally joined this fight: artificial intelligence (AI). In Rwanda, we collaborated with the government to develop and deploy an Mpox skin image detection AI tool, enabling health workers to use mobile devices at key border entry points with Uganda, Burundi and the DRC. The AI skin test complements molecular screening methods to drive early detection, outbreak control and protection of vulnerable groups at risk of serious Mpox cases.

Teaching an algorithm to screen and support surveillance is transferable to other infectious diseases, tailored to contexts and populations. Global Fund-financed countries have deployed AI since 2018 through health ministries and communities to expand healthcare access, increase testing volumes and guide health workers to where their work is most needed to manage tuberculosis. This existing base can be expanded, at a reasonable cost, to screen for other infectious diseases too.

For this next era of AI innovation, the Global Fund and other multilaterals must balance the push to deploy cost-effective AI tools across the health sector, while maintaining investment in good, secure digital foundations. Health systems operate across the care continuum from diagnosis to supporting triage and treatment to decision-support, even in countries where there is finite human and infrastructural capacity.

As artificial intelligence reshapes every industry, the Global Fund works with countries to ask critical questions relevant to the health sector:

What are the challenges we are facing?

Which AI applications make sense today?

How can they be deployed to accelerate progress?

How can they be scaled?

The answers lie in a clear theory of change. By adopting new technologies, countries have successfully aligned innovations to their digital maturity and financial readiness, advancing in step with their capacity, rather than waiting for the 'perfect' foundations or leaping too far ahead.

AI tools must be designed for scale, meaning moving beyond single-use apps towards modular, open-architecture platforms. From the start, pilots should be embedded within health ministries or national digital-health platforms and built with interoperable components that can be adapted for new diseases, geographies, or functions.

For this vision to be realized, we must continue investing in the foundations of quality data and digital systems. We hold AI tools to the same rigorous standards and accountability frameworks that guide other health investments, supporting countries in adopting tools that are tailored to their existing systems and maturity. While AI has incredible potential, it is also a completely new way of operating that needs to be introduced through secure, reasoned, ethical, evidence-based scaling.

Countries with basic digital infrastructure – such as reliable power, health facility-level connectivity and digitized patient registers – can already implement simple AI tools. These include: triage chatbots, dashboard stock-out alerts or AI-driven text message reminders.

As the digital systems of a country mature, more advanced applications can be deployed, such as predictive surveillance, automated image diagnostics and optimized healthcare administration and workforce productivity apps. Countries further along the maturity curve – with interoperable national health information exchanges, stable broadband and maintenance budgets – can consider advanced analytics, such as real-time outbreak forecasting or precision resource allocation models.

This staged approach ensures each country advances in its use of AI at a pace it can sustain and scale; the availability of a robust basic digital infrastructure is what enables Rwanda to use its Mpox skin image detection AI tool.

Despite the obvious benefits of these investments, lower-income countries face high borrowing costs and constrained budgets. Concessional financing and grants from multilaterals can be supported by mobilizing pooled funds, securing preferential pricing on hardware and software and aligning institutional financing with national digital-health strategies and data standards.

By underwriting shared infrastructure, such as solar-powered servers, regional analytics hubs or last-mile networks, these institutions absorb much of the initial risk, giving countries the runway they need to integrate AI into public systems, without shouldering the full financial burden up front. Of course, these must enable countries to take clear ownership and facilitate full sustainability, regardless of the initial funding source.

To harness the power of AI for global health, institutional funders and governments can adopt these four key principles:

1. Assess readiness against maturity

Evaluate every AI proposal based on where a given country sits on digital maturity and financial readiness curves.

2. Phase ambition

Match the complexity of AI use cases to maturity levels. Start with simple tools, then extend to advanced AI as systems and budgets strengthen.

3. Mandate public–private co-investment

The major AI innovations are driven by the private sector. Design funding so that governments, multilateral funding institutions and private partners share costs, expertise and risk and operate in a functional and sustainable digital marketplace.

4. Plan for ownership at each stage

Define clear handover roadmaps that guide the transition from pilot to scaled platform, aligned with each phase of maturity.

When AI adoption matches a country’s evolving capacity and when multilateral funders convene all actors to share risk and build shared infrastructure, we unlock technology’s true promise: smarter, more equitable health systems that can both end major diseases and endure. It’s time to move beyond novelty towards solutions that work at scale and leave no one behind.