Innovating for Health Equity

Artificial intelligence (AI) could transform frontline health systems in low- and middle-income countries — supporting community health workers as well as the people they serve. Yet, as health innovations accelerate, inequities persist. Billions still live far from trained clinicians, reliable supply chains, or even stable electricity.

The global challenge is not only to develop smarter technologies but to ensure that they reach and strengthen the places where health equity is won or lost — in local communities. Properly designed AI can turn fragmented and under-resourced systems into responsive, adaptive learning systems, but this is only possible when equity and trust are embedded from the start.

As WHO’s guidance on ethics and governance of AI in health emphasizes, innovation must respect human rights, equity, and solidarity. The principle of “Nothing about us without us” must apply as much to algorithms as to policies.

This was a key message at this year’s World Health Summit, in a session co-organized with the Global Solutions Initiative. Discussions highlighted both the promise of AI and the responsibility to govern its development and application in ways that genuinely serve communities.

 

AI in Low-Resource Health Systems

In settings where one doctor may serve tens of thousands, AI can support non-specialist providers in making more accurate and timely decisions. These expectations have led to a new focus throughout low- and middle-income countries on creating digital infrastructures that enable health workers to use smartphones as diagnostic tools and provide online support and consultations.

See the Stanford roundtable summary on AI for Health in LMICs.

Many examples already exist of AI helping health workers recognize danger signs in pregnancy, child health, malaria, or neglected tropical diseases (NTDs). To move beyond isolated pilots, several African countries have developed comprehensive digital health strategies.

One example discussed at the Summit was Tanzania’s Digital Health Strategy 2019–2024, designed to accelerate progress toward Universal Health Coverage (UHC) while identifying practical challenges in the digitalization of the health sector.

Tanzania also developed a Digital Health Investment Road Map (2017–2023), bringing donors and implementation partners together to align support, funding, and technical expertise. Some of the most promising applications of AI are found in primary care and community settings, and current efforts focus on ensuring that digital health systems are implemented in a well-coordinated and interoperable manner.

 

Building Data-Driven Community Clinics

Many countries are working toward data-driven community clinics — local hubs where predictive algorithms can anticipate disease outbreaks and enable rapid responses. Crucially, AI can democratize access to health knowledge and services, improve care quality, and strengthen community-based health promotion and prevention programs.

However, AI will not automatically make health systems fairer. Without careful design and governance, it risks reinforcing existing inequities. Some of the key challenges include:

• Unequal access to digital tools and AI innovations, including large language models (LLMs).
• Bias in datasets that may not represent local populations.
• Data extraction and protection issues, which can threaten data sovereignty.
• Market-driven agendas that sideline public-interest goals and undermine equity commitments.

For AI to contribute meaningfully to health equity, it must be trained on inclusive, locally relevant data, adapted to local contexts, and deployed within systems that are transparent, accountable, and participatory.

Essential prerequisites include connectivity, reliable electricity, secure data infrastructure, and frontline workers’ skills to critically engage with AI tools. For more guidance, see WEF’s Blueprint for Equity and Inclusion in AI.

 

Addressing the Equity Challenge

Key elements highlighted at the Summit for equitable AI deployment include:

1. Inclusive data: Algorithms trained primarily on Western datasets may fail in African or Asian contexts. Local data must guide design.
2. Digital infrastructure: Connectivity, reliable power, and secure data storage are prerequisites.
3. Capacity building: Frontline workers must be trained to use, question, and interpret AI tools.
4. Ethical governance: Transparency, accountability, and community participation must guide deployment.

 

AI and Health Financing

As we look at front-line applications of AI, the relevance of overhauling and digitalizing payment systems must not be underestimated. Over the past decade, several African countries — Rwanda, Nigeria, Kenya, Tanzania, and Ethiopia — have made significant strides in expanding health insurance coverage. This progress has played a crucial role in improving access to affordable healthcare services across the continent.

Artificial intelligence (AI) supports these systems by enhancing claims processing, detecting fraud, and ensuring rapid payments to providers and beneficiaries (ScienceDirect; Amref Blog).

 

AI Innovation and Digital Health Integration

Amazon Web Services (AWS) recently announced a $10 million fund to support nonprofits in using cloud technology and artificial intelligence (AI) to tackle health disparities in Africa (AWS Nonprofits).

AWS is also introducing a Health AI Hub, specifically designed for healthcare organizations in Europe, the Middle East, and Africa (AWS Health AI Hub). The Hub aims to empower healthcare providers, payors, researchers, and government agencies to explore, test, build, and deploy generative AI solutions rapidly.

 

Toward Scalable and Ethical Digital Health

Integrating digital health into Universal Health Coverage (UHC) requires alignment across multiple domains:

• Health communication
• Service delivery
• Product and technology management
• Monitoring and evaluation
• Health financing

For AI to truly strengthen frontline health systems, innovation ecosystems must evolve — moving beyond pilot projects toward scalable and sustainable solutions. Above all, health information integrity must be ensured (WHO Guidance).

Governments play a key role by aligning digital health strategies with social protection and financing models, embedding AI into prevention and primary care, and co-designing solutions with communities to ensure that technology addresses real needs and cultural contexts.

Cross-country collaboration is essential. By learning from each other, nations can address common challenges around data management, implementation, and ethical concerns. Low- and middle-income countries remain deeply concerned about data extraction, loss of data sovereignty, and health sovereignty. Regional bodies such as Africa CDC support member states in leveraging digital health while safeguarding sovereignty, as outlined in its 2023 Digital Transformation Strategy.

 

Conclusion

AI is already transforming health systems. The critical question is whether it will advance health equity. Success depends on inclusive, values-driven partnerships that bridge innovation, governance, and local capacity — placing people and communities at the center of health system transformation.