The Research and Innovation Directorate (RID) is pleased to inform the University community of a funding opportunity under the Evidence for AI in Health (EVAH) Initiative, delivered by the Abdul Latif Jameel Poverty Action Lab (J-PAL) in partnership with the African Population and Health Research Center (APHRC) and supported by the Wellcome Trust, the Gates Foundation, and the Novo Nordisk Foundation. The initiative seeks to support rigorous evaluations of artificial intelligence (AI)–enabled clinical decision support tools (CDSTs) used by frontline community and primary healthcare workers in low- and middle-income countries, including those in Sub-Saharan Africa.

 Purpose and Scope

The call aims to generate robust evidence on the effectiveness, safety, and equity implications of AI tools used in primary and community healthcare systems. Proposals should evaluate AI-enabled tools that support frontline healthcare workers in tasks such as triage, diagnosis, or referral, particularly in resource-constrained settings.

Projects should:

• Evaluate AI-enabled clinical decision support tools integrated into frontline health worker workflows
• Generate real-world evidence on performance, adoption, and health system outcomes
• Inform policy and scale-up decisions for digital health technologies in LMICs.

 Funding and Duration

Two funding pathways are available:

Pathway A – Real-World Deployment and Systems Integration Evidence

• Funding: Up to USD 1,000,000
• Duration: 3–12 months

Pathway B – Real-World Impact and Health System Evidence

• Funding: Up to USD 3,000,000
• Duration: 12–24 months

Budgets should align with the scale, duration, and complexity of the proposed research.

 Eligibility

Eligible applicants include researchers and institutions capable of conducting rigorous evaluations of AI tools in healthcare settings. Projects must focus on low- and middle-income countries in Sub-Saharan Africa, South Asia, or Southeast Asia and involve tools that are already in deployment or ready for real-world implementation.

 Application Timeline

• Proposals Due: 1 April 2026 at 10:00am Eastern Daylight Time
• Anticipated notification: June 2026

 For full call details and application information, kindly see the attached document or visit: https://www.povertyactionlab.org/initiative/evidence-ai-health-evah-rfp

 RID encourages researchers in public health, medicine, computer science, artificial intelligence, health economics, and digital health to explore this opportunity. Interested applicants are kindly requested to inform RID of their intention to apply to enable institutional guidance and support. Please email rid-grantsmgt@ug.edu.gh, with jobappiah@ug.edu.gh in copy.