This discussion paper presents an emergent research landscape that explores linkages between artificial intelligence (AI) solutions and global health. It identifies critical evidence gaps and outlines opportunities to leverage AI solutions responsibly to reduce health inequity and strengthen health systems.

From clinical medicine to public health, AI solutions are advancing how infectious and noncommunicable diseases are handled in terms of diagnostics, preventative care, healthcare planning and delivery, clinical decision-making and care delivery, public health surveillance, drug discovery and development, and responses to health threats. Evidence to guide the practice and use of AI in global health is struggling to keep pace with AI evolution and application. There is no time to waste.

Responsible development and use of AI solutions in a global health context must be predicated
on research objectives that address knowledge gaps, consider ethical implications, focus on the
needs of underserved populations, target neglected conditions and bring a Global South perspective
to the forefront.

The proposed research landscape aims to achieve these objectives by responding to global and health-specific trends, which have a pronounced impact on the research environment. Furthermore, the
research landscape offers cross-cutting prerequisites for research, including regulation, policy and
governance; data quality and representation; gender equality and inclusion; ethics and sustainability;
and Global South-led and equitable partnerships.

This discussion paper presents three proposed entry points for AI and global health research: health
services (for example, the health workforce), community (for example, One Health surveillance and
solutions), and individual health (for example, self-care). Underpinning the research landscape is the
argument that evaluation is a critical requirement at every stage of AI development, deployment and
adoption for use, and that scaling AI solutions is a choice that should be carefully considered,
intentionally charted and informed by grounded research. When done responsibly, scaling offers
extraordinary opportunities to address vulnerabilities and improve lives. Finally, the research landscape
aims to ensure impact with evidence and solutions that lead to stronger and more resilient health
systems.

Using AI in health settings can lead to outcomes that either reduce or deepen inequities. By advancing
the research agenda in a deliberate and strategic manner, the Global South should lead with its
expertise and evidence to help shape their own AI solutions that are equitable, safe, rights-based,
inclusive and sustainable. Donors and research support organizations also have a key role to play in
ensuring AI does not perpetuate inequalities or trample people’s autonomy and agency.

This research landscape is a starting point for discussion, exploration and experimentation. With a 2030
deadline to meet the Sustainable Development Goals and advance wellbeing for all, this research
landscape offers a roadmap to guide discussions and action among the global research community.

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