African Data for African Health: Building the AI Foundations the Continent Needs
🎙️ Episode Title
African Data for African Health: Building the AI Foundations the Continent Needs
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🧠 Episode Summary
In this episode, Oliver Morgan speaks with Agnes Kiragga, Head of the Data Science Program at the African Population and Health Research Center in Nairobi, Kenya. Drawing on over two decades of experience building data systems and leading large-scale multi-country initiatives across the continent, Agnes reflects on what it takes to make AI work in African public health — not as a concept, but in practice, inside real institutions with real constraints. The conversation spans the full range of Agnes's work: from no-code AI platforms that bring real-time analytics to researchers without coding backgrounds, to AI-powered blood stock management systems in Cameroonian hospitals, to federated analysis platforms that allow data to be analyzed across borders without ever leaving its home institution.
The episode addresses one of the most consequential gaps in global health AI: the underrepresentation of African datasets in the models being built for epidemic intelligence. Agnes argues that tools not trained on African data cannot capture the comorbidity burden, the linguistic diversity, or the epidemiological reality of African populations — and that the consequences range from missed diagnoses to wrong predictions during outbreaks. She also makes the case for a new generation of evaluation frameworks for large language models in African health contexts, where multi-turn, multilingual interactions are the norm, not the exception. Throughout, Agnes is clear-eyed about what is still missing — governance structures, infrastructure, skilled personnel — while pointing to the momentum building across the continent in training, tooling, and institutional capacity. Her core argument: AI for African health cannot be imported. It has to be built, evaluated, and owned from within.
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💬 Guest
Agnes Kiragga is Head of the Data Science Program at the APHRC in Nairobi, Kenya. With over 20 years of experience working with large and diverse health datasets across Africa, she leads the Data Science Without Borders project. She also leads the INSPIRE Network, a consortium of approximately 25 longitudinal Health and Demographic Surveillance System sites across Africa. Her work spans data harmonization, federated analysis, AI governance, and the responsible evaluation of AI tools in low- and middle-income country settings. She is a delivery partner for the Evidence for AI in Health initiative, which generates rigorous evidence on whether AI tools actually work in the contexts where they are deployed.
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🌐 Resources and References
- APHRC: https://aphrc.org/
- DSWB: https://dswb.africa; https://doi.org/10.3389/fpubh.2025.1695907
- INSPIRE Network: https://www.inspiredata.network/; https://aphrc.org/inspire/; https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2024.1329630/full
- DataSHIELD: https://assets-eu.researchsquare.com/files/rs-9149238/v1/30a60413-de69-4c32-9237-577e7690944c.pdf?c=1776244497
- EVAH: https://www.povertyactionlab.org/initiative/evidence-ai-health-evah-initiative
- AfriMed-QA: https://afrimedqa.com/
- OHDSI common data model: https://www.ohdsi.org/
- AIMS: https://aims.ac.za/
- Data Science Africa: https://www.datascienceafrica.org/
- Deep Learning Indaba: https://deeplearningindaba.com/
- IDRC AI for Development: https://www.idrc.ca/en/initiative/artificial-intelligence-development
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🎵 Music Credits
Track: ‘Nairobi Nights’. License code: WPJKGDX60OHV0I2I.
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⚠️ Disclaimer
This podcast is produced by the World Health Organization (WHO) as part of the Pandemic and Epidemic Intelligence Innovation Forum initiative. The views expressed by guests are their own and do not necessarily represent those of WHO or its affiliates. Content is intended for informational purposes only and does not constitute professional medical advice.
