Uganda's Ministry of Health, with Medic and Audere, built AI malaria rapid-test interpretation into the national eCHIS across four districts; a blinded four-month Phase I found high agreement with Village Health Teams, though no percentages are published yet.
Details
Maturity
Pilot
Promoter
Ministry of Health (Uganda), with Medic and Audere
Period
2026
Keywords
public health, malaria diagnostics, community health workers, computer vision
Context
Malaria rapid diagnostic tests (RDTs) are read visually by community health workers, and misreadings — especially faint positive lines — are a persistent source of misdiagnosis and surveillance error in high-burden districts. Uganda's Ministry of Health, together with the global digital-health nonprofit Medic and health-AI company Audere, integrated Audere's HealthPulse AI Interpreter — a computer-vision tool that photographs and automatically classifies RDT results — into the national electronic Community Health Information System (eCHIS). The pilot runs in four districts (Mpigi, Moyo, Buikwe and Lira), with support from the Gates Foundation and a parallel deployment with Kenya's Ministry of Health.
Activities
Phase I, running roughly four months to August 2026, deliberately withheld the AI's interpretation from Village Health Teams so their manual reading could be compared against the AI's result without bias. Phase II, a three-month follow-on, will for the first time show Village Health Teams the AI's interpretation alongside their own, testing whether visible AI assistance improves decisions or simply displaces human judgement.
Results
A feedback and synthesis workshop following Phase I reported 'very high agreement' between the AI and Village Health Team readings and growing user confidence, but no agreement percentage, sample size or case count has been made public.
Implementation
Indicative cost
Low (< €50k)
Time to results
Short (< 1 year) — Phase I ran approximately four months to August 2026 (blinded); Phase II is a three-month follow-on.
Staffing & skills
Uganda Ministry of Health, Medic (global digital-health nonprofit) — technical partner, Audere (health-AI company) — technology provider, Village Health Teams (community health workers) — end users
Conditions for success
Blinded design in Phase I to avoid biasing Village Health Team manual readings
Integration directly into the existing eCHIS platform already used by community health workers
Parallel deployment with Kenya's Ministry of Health for cross-country learning
Common failure modes
No agreement percentage, sample size or case count made public after Phase I
Phase II has not yet tested whether visible AI assistance improves decisions or merely displaces human judgement
Where it fits
Governance type
national ministry of health with NGO/vendor partners
Scale
sub-national (4 districts)
Income level
low income (Uganda)
Commonly funded by
Philanthropic / foundation funding
Indicative funding routes for practices of this type — always check each programme's current calls and eligibility rules.
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Data sources
Where this practice's information was retrieved from, and when.
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