Request for Proposals: Evidence for AI in Health Initiative

Deadline: 01-Apr-2026

The Evidence for AI in Health initiative is inviting proposals to evaluate AI-enabled Clinical Decision Support Tools (CDSTs) used in primary and community health care settings. Funding is available through two pathways: up to $1 million for early-stage real-world evaluations and up to $3 million for rigorous impact evaluations at scale. Eligible applicants must be legally recognized entities operating in Sub-Saharan Africa, South Asia, or Southeast Asia with regional leadership and strong evaluation expertise.

The Evidence for AI in Health call supports locally led evaluations of AI-enabled Clinical Decision Support Tools (CDSTs) designed to assist frontline health workers.

The goal is to generate decision-relevant evidence on how AI tools can be responsibly integrated into primary health care systems and scaled effectively.

What Are AI-Enabled Clinical Decision Support Tools (CDSTs)?

AI-enabled CDSTs are digital health tools that use artificial intelligence to assist health workers with:

• Diagnosis support

• Treatment recommendations

• Triage decisions

• Referral guidance

• Risk assessment

These tools may use:

• Text inputs

• Voice-based interfaces

• Image analysis

• Multimodal systems combining multiple inputs

They are typically used in primary health care and community settings where frontline workers require timely clinical guidance.

Core Objectives of the Call

This funding opportunity aims to:

• Strengthen integration across levels of care

• Improve interoperability with digital health systems

• Enhance referral processes and service coordination

• Support responsible integration of AI into health systems

• Reduce inequities by expanding reach to vulnerable populations

• Improve outcomes for high-burden conditions

Proposals should generate actionable evidence for:

• Ministries of Health

• Health system implementers

• Funders and development partners

Funding Pathways

Applicants must choose one of two pathways.

Pathway A – Early Deployment Evaluation

Funding: Up to USD $1,000,000

Target Tools: AI-enabled CDSTs that are early in deployment

Focus Areas:

• Usability and user experience

• Workflow integration

• Adoption and acceptability

• Safety assessment

• Feasibility in real-world settings

Purpose: To generate implementation evidence that informs future large-scale impact evaluations.

Pathway B – Rigorous Impact Evaluation

Funding: Up to USD $3,000,000

Target Tools: AI-enabled CDSTs ready for scale

Focus Areas:

• Measurable health outcomes

• System performance indicators

• Service delivery efficiency

• Quality of care improvements

Purpose: To determine the causal impact of AI-enabled CDSTs at scale and inform national or regional expansion decisions.

Priority Research Themes

Proposals are especially encouraged if they:

• Strengthen integration of service delivery across levels of care

• Assess interoperability with digital health tools and data systems

• Evaluate integration of regulated health commodities where permitted

• Explore multimodal AI applications

• Address rural, underserved, or marginalized populations

• Focus on high-burden diseases with potential for significant improvement

Who Is Eligible?

Eligible applicants must meet all of the following criteria.

Geographic Eligibility

• Must operate in Sub-Saharan Africa, South Asia, or Southeast Asia

• Principal Investigator (PI) or Project Lead must be based in the region

Legal Status

• Must be a legally recognized entity

Eligible organization types include:

• Nonprofit organizations

• For-profit companies

• International organizations

• Government agencies

• Academic institutions

• Multi-sector consortia

Partnership Requirements

Applications must include partners responsible for:

• Clinical implementation

• Overall project management

• Evaluation design and execution

Technical Expertise

Teams must demonstrate:

• Expertise in health systems research

• Experience conducting impact evaluations

• Capacity for rigorous study design and analysis

What Projects Must Demonstrate

Successful proposals must:

• Access to an AI-enabled CDST already deployed or ready for deployment

• Strong evaluation framework

• Clear research questions aligned with decision-making needs

• Ethical and responsible AI considerations

• Relevance to Ministries of Health and national health strategies

Why This Call Matters

AI tools are increasingly being introduced into primary health care, but many lack rigorous evidence on:

• Safety

• Effectiveness

• Equity impact

• System integration

This call aims to close that evidence gap by supporting locally led research that informs:

• Responsible AI adoption

• Health system strengthening

• Future scale-up decisions

It prioritizes evidence generation in regions with high disease burden and constrained health system resources.

How to Apply

Step 1: Confirm Tool Readiness
Ensure your AI-enabled CDST is either early in deployment (Pathway A) or ready for scale (Pathway B).

Step 2: Form a Qualified Consortium
Include partners responsible for:

• Clinical implementation

• Evaluation

• Project management

Step 3: Define Evaluation Framework
Clearly outline:

• Study design

• Outcome indicators

• Data collection methods

• Ethical safeguards

• Integration strategy

Step 4: Align with Policy Needs
Demonstrate how findings will inform:

• Ministry of Health decisions

• National digital health strategies

• Future scale-up pathways

Step 5: Submit Complete Proposal
Ensure eligibility criteria are met and documentation is complete.

Common Mistakes to Avoid

• Proposing tools without real deployment access

• Weak evaluation methodology

• Lack of regional leadership

• Insufficient health systems research expertise

• Vague policy relevance

• Failure to address interoperability or equity

Strong proposals clearly link evaluation findings to real-world decision-making.

H2: Key Definitions

• Clinical Decision Support Tool (CDST)
  A digital system that provides clinicians or frontline health workers with evidence-based guidance during patient care.
• Interoperability
  The ability of digital systems to exchange and use data across platforms and institutions.
• Impact Evaluation
  A rigorous research design that measures causal effects of an intervention on predefined outcomes.
• Primary Health Care
  First-level contact care provided in community or local health facilities.

H2: Frequently Asked Questions (FAQ)

1. How much funding is available?
   Pathway A offers up to $1,000,000. Pathway B offers up to $3,000,000.

2. Who can apply?
   Legally recognized entities operating in Sub-Saharan Africa, South Asia, or Southeast Asia with regional leadership.

3. Can international organizations apply?
   Yes, if they meet regional and legal eligibility requirements and include appropriate local leadership.

4. Must the AI tool already exist?
   Yes. Applicants must have access to a CDST that is deployed or ready for deployment.

5. What is the difference between Pathway A and Pathway B?
   Pathway A focuses on implementation and usability. Pathway B focuses on rigorous measurement of impact.

6. Is partnership mandatory?
   Yes. Applications must include partners for clinical implementation, project management, and evaluation.

7. What types of conditions are prioritized?
   High-burden health conditions where AI-enabled tools can significantly improve outcomes.

Conclusion

The Evidence for AI in Health call provides a major opportunity to generate high-quality, locally led evidence on AI-enabled clinical decision support tools in primary health care systems.

By funding both early-stage implementation research and large-scale impact evaluations, this initiative supports responsible AI integration, improved health outcomes, and evidence-based policy decisions across Sub-Saharan Africa, South Asia, and Southeast Asia.

For more information, visit Abdul Latif Jameel Poverty Action Lab (J-PAL).