India and WHO seek proven AI health use cases from the Global South
IndiaAI (under MeitY) and the World Health Organization have opened a call for research abstracts on scalable AI in healthcare from the Global South. Selected case studies will be published in the Casebook on AI Health Use Cases Across the Global South and launched at the India-AI Impact Summit 2026 in New Delhi.
The focus is simple: document real implementations that improved care delivery, strengthened system efficiency or expanded access-especially in resource-limited settings. Preference will go to models that are replicable and already showing measurable outcomes.
What the Casebook will highlight
- Proven AI applications in disease surveillance, diagnostics, data management and equitable access to care.
- Deployment lessons: governance, integration, change management and workforce enablement.
- Impact evidence: clinical outcomes, operational gains, cost metrics and equity indicators.
- Barriers and enablers so other systems can implement with fewer risks.
Who should submit
Teams running real AI deployments in Asia, Africa, Latin America or the Pacific. This includes health ministries, public and private providers, research institutions, NGOs, startups and consortia.
The expectation: work that goes beyond pilots, is implementable at scale and can be adapted by peers facing similar constraints.
Submission details
- Abstract length: up to 250 words.
- Deadline: 31 October 2025.
- Scope: successful, scalable and implementable AI use cases in healthcare.
- If shortlisted: full chapter of 2,500-3,000 words covering the solution, deployment process, ethical considerations, outcomes and lessons learned.
What "scalable" means in low-resource settings
- Replicable across facilities or regions with limited additional cost.
- Works with available infrastructure and minimal specialized hardware.
- Clear governance for data privacy, security and consent.
- Fits into clinical workflows and reduces rather than adds burden.
- Supports equity: local language support, inclusive datasets and measured access gains.
- Documented costs and a sustainable funding path.
Examples of relevant problem areas
- Early warning and disease surveillance.
- Diagnostics and triage (imaging, pathology, point-of-care decision support).
- Supply chain forecasting and inventory management.
- Telehealth triage, referral optimization and virtual care routing.
- NCD management, maternal and child health, and outbreak response.
- Data quality, interoperability and health information systems.
Why this matters for healthcare leaders
- Access field-tested models instead of theory.
- Shorten procurement cycles with clearer evidence and risk controls.
- Learn what it really takes to operationalize AI: policy, people, process and technology.
- Build South-South collaboration and avoid duplicating effort.
Timeline and launch
The Casebook will be launched at the India-AI Impact Summit 2026, scheduled for 19-20 February 2026 at Bharat Mandapam, New Delhi. Expect a practical reference for health leaders, regulators and implementers planning AI programs in 2026 and beyond.
How to craft a strong abstract
- Problem and baseline: the gap, who it affects and why it matters.
- Intervention: AI method, data sources, workflow integration and oversight.
- Ethics and safety: privacy, bias mitigation, human-in-the-loop and audit trails.
- Results: clinical outcomes, operational impact, equity metrics and costs (before/after).
- Scale: where it's live, required enablers, portability to other contexts.
- Lessons: what failed, what changed, what you would repeat or avoid.
About the partners
The Casebook is a joint effort by the IndiaAI Mission under MeitY and WHO's Department of Digital Health and Innovation. Both have contributed to global guidance on responsible AI in health and digital transformation policy.
Global South: scope
The Global South includes developing and emerging economies across Asia, Africa, Latin America and the Pacific. The Casebook focuses on solutions adapted to constraints common in these settings-workforce shortages, infrastructure gaps and varied data ecosystems.
Quick eligibility check
- Project implemented in a Global South context.
- Evidence of impact beyond a limited proof-of-concept.
- Clear pathway to scale and adoption in comparable settings.
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