Evidence, tools and country experience for ministries, funders and implementers, curated around the interconnected foundations mapped in Vital Strategies’ Foundations & Futures report.
What it takes for health systems and the wider digital ecosystem to be ready to adopt and scale AI in public health.
Government-led, digitized, and interoperable systems (surveillance, registries, CRVS, facility and workforce records) that generate reliable, continuous health data before any AI layer can be built on top.
The authority, coordination, and oversight capacity public institutions need to select, deploy, monitor, and hold AI tools accountable.
The digital literacy, AI/ML skills, and workforce trust required for health workers and institutions to actually use AI tools effectively.
Stable, long-term funding that treats data and digital systems as public infrastructure, not one-off pilots, so tools survive beyond initial project funding.
Shared national systems (identity, digital payments, data exchange layers) that let health intelligence connect across sectors instead of staying siloed.
The rules and safeguards governing privacy, consent, secondary data use, and accountability that make data sharing and AI use safe and legitimate.
The mechanisms that let signals detected in one sector (health, education, social protection) translate into a joined-up, timely state response.
What AI can enable in public health once foundational systems are in place.
Machine learning models that speed up detection of diseases like TB, cancer, and COVID from images and scans, easing specialist workload in under-resourced settings.
Decision-support tools embedded in everyday care that help clinicians and community health workers flag risk and prioritize action.
Generative AI and chatbots that deliver culturally responsive, localized health information to shift behavior at scale.
AI applied to administrative and geospatial data to forecast demand, optimize resource distribution, and target underserved communities.
Predictive analytics and anomaly detection that spot outbreaks and emerging risks earlier, enabling faster public health response.
Built on CRVS, digital ID, and integrated health data systems, Rwanda’s NHIC turns continuous, government-led population data into intelligence for surveillance, forecasting, resource allocation, and AI use grounded in public systems rather than fragmented project data.
ABDM is India’s foundational layer for AI-ready public health. eSanjeevani, CoWIN, and emerging AI tools build on shared records, consent, registries, and reusable data flows, turning digital projects into infrastructure for care, vaccination intelligence, and planning.
Rio’s CIE connects millions of health and city records into an integrated data environment, helping teams detect outbreaks, forecast respiratory illness, guide extreme heat response, and turn interoperable data systems into coordinated public action before communities are harmed.
Carried by digitized primary care, hospital, mortality, and violence data, ClarIA shows how AI can surface hidden risks for gender-based violence and suicide before harm escalates, using routine public health systems and linked care journeys as its foundation.
This Technical Learning Note explores how countries across the Global South are designing and implementing trusted and inclusive digital ID systems that advance public health. Drawing upon a PILLARs Peer Learning Exchange with more than 350 participants from 83 countries, it synthesizes practical challenges, adaptations and lessons for AI-enabled public health.
This practical guide helps public health leaders and implementation teams plan, govern and implement data linkage. It outlines the decisions that shape a linkage investment, the steps to produce reliable, linked information, and the opportunities this creates for AI-enabled public health. It focuses not only on completing a linkage exercise, but also on building capabilities institutions can sustain and reuse.
An overview of CODA, a three-year initiative developing and testing an AI-enabled resource to improve cause-of-death determination where clinical information is limited or unavailable. Led by a global consortium coordinated by Vital Strategies, the project aims to strengthen mortality intelligence in low-resource settings and help governments better understand why people are dying.
