Harnessing AI to advance efficient and equitable health care in Sub-Saharan Africa

Artificial intelligence (AI), if implemented responsibly, could help accelerate health sector investments in Sub-Saharan Africa by improving efficiency, equity, and transparency in health financing. At the recent Universal Health Coverage High-Level Forum in Tokyo on December 6, 2025, countries announced 15 National Health Compacts, many of which prioritize digital transformation and the use of digital tools to strengthen health systems. This matters because the region continues to experience low investment in health: In 2022, governments spent on average only 2% of GDP on health (Figure 1)—less than half the aspirational target of 5% and far below regions like North America (9%) and Europe and Central Asia (7%). 

Figure 1: Domestic general government health expenditure (% of GDP)

Source: World Bank Development Indicators

Persistent financing gaps

Recent World Bank (2025) analysis suggests that that achieving universal health coverage (UHC) requires about $60 per capita in low-income countries (LICs) and $90 in lower-middle-income countries (LMICs). In 2024, median per‑capita government health financing plus off‑budget donor support is about $17 in LICs and $47 in LMICs, implying financing gaps of roughly $43 per capita. These benchmarks, while global, are highly relevant for Sub-Saharan Africa where most countries fall into these income groups.

This underinvestment has contributed to limited access to essential services, high rates of preventable diseases, and significant financial hardship for households. Despite the Abuja Declaration’s pledge to allocate at least 15% of national budgets to health, most countries in Sub-Saharan Africa countries remain well below this benchmark, averaging about 11% in the most recent year available (2019). Compounding the challenge, donor health funding is declining, and domestic spending is not rising quickly enough to close the gap.

Why AI matters for health financing

Effective health financing is essential for building resilient health systems and achieving UHC. AI offers powerful tools to enhance the three core functions of health financing: revenue collection, pooling, and purchasing.

1. AI can support strategies to increase revenue

AI is useful for planning and allocation in health financing. AI can improve the accuracy of forecasting future health funding needs compared to traditional statistical models. While simulation models for policy reforms such as introducing a health tax, expanding insurance coverage, or redirecting fuel subsidies have long existed, AI enhances these models by processing larger, more complex datasets and generating faster, more adaptive predictions. These capabilities help governments plan strategically and allocate resources more efficiently. Beyond allocation, AI can also support compliance in tax administration by detecting anomalies and improving enforcement.

In Nigeria, banks and other financial institutions are beginning to use AI to better understand and manage credit risks, as well as to forecast future financial trends. Similarly, AI techniques can strengthen revenue collection in health financing. For example, insurers and health purchasing agencies can apply predictive models to estimate premium revenues, identify patterns of delayed or missed contributions. Scenario simulations can help stress-test revenue streams under different policy changes, such as adjustments in premium rates or subsidy structures and ensuring greater predictability and stability.

2. Using funds where they’re needed most

Risk pooling is designed to protect people financially and promote fairness in accessing health services. AI can analyze complex data covering demographics, socioeconomic factors, and health information with the aim to pinpoint poor and vulnerable groups who would benefit most from targeted subsidies or insurance coverage. Thus, AI could help segment populations based on risk, health status, and economic indicators, helping governments direct support where it is most needed. For instance, World Bank analyses show that targeting subsidies to poorer populations can improve equity in pooled funds.

Furthermore, integrating free maternal and child health programs into national health insurance schemes in Sub-Saharan Africa offers an opportunity to enhance equity and sustainability. While universal entitlement ensures access for all, it often strains resources and leaves the poorest vulnerable to hidden barriers. Leveraging AI can transform this approach by enabling precise targeting of subsidies, predicting households at risk of catastrophic health spending, and optimizing resource allocation. By combining social registry data, geospatial indicators, and health utilization patterns, AI-driven systems can help governments maintain universal coverage while prioritizing support for those most in need.

For example, the World Bank partnered with the Government of Togo to improve how cash assistance was targeted during the COVID-19 response under the Novissi program, working alongside UC Berkeley and GiveDirectly. The approach combined innovative use of mobile phone data and satellite imagery to identify households most in need, achieving far greater accuracy than traditional geographic methods. Findings from this effort, published in Nature in 2022, showed significant improvements in reaching vulnerable populations. The pilot reduced exclusion errors and enabled faster, contactless delivery of support, helping inform ongoing efforts to strengthen social protection systems across West Africa.

3. Linking payments to results

AI can enable health systems to link payment directly to patient outcomes. By looking at clinical results, costs, and population health data, AI can help track how well providers are doing, match payments to the actual needs of communities, automate complex calculations, and even spot potential fraud. Researchers used a machine-learning method called Random Forests to predict high-risk health facilities for audits in Zambia's performance-based financing program, achieving 88% accuracy and cutting verification costs by about two-thirds compared to random sampling, making audits smarter, cheaper, and more efficient.

Figure 2: Smarter health financing through AI

To fully harness AI in health financing and advance the goal of delivering affordable, quality health services to 1.5 billion people by 2030, governments in Sub-Saharan Africa must invest in digital infrastructure, data quality, and workforce capacity, while ensuring ethical and responsible AI use. By embracing AI, governments can make health financing more efficient, equitable, and transparent, laying the foundation for stronger health systems. The World Bank's Mission 300, aiming to connect 300 million people to power by 2030, is paving the way for digital transformation.