Non-Communicable Diseases (NCDs) diabetes, hypertension, cardiovascular disease, chronic respiratory diseases, and cancer are the world's leading cause of death. They claim 41 million lives every year, accounting for 71% of all global deaths.
And yet, the conversation about global health continues to centre on infectious diseases.
This is not an accident. Infectious diseases are dramatic. Outbreaks make headlines. NCDs, by contrast, are slow, silent, and cumulative. They develop over years. They kill quietly. They are easy to ignore until they aren't.
The Africa Paradox
Africa is living through a paradox. On one hand, infectious diseases remain a genuine threat. On the other, NCDs are rising at a pace that most health systems are not prepared for.
In Rwanda a country often held up as a model for health system transformation NCDs now account for 44% of all deaths (WHO, 2024). Hypertension affects approximately 15.3% of adults. Diabetes affects 4.9%. Most of these cases are diagnosed only when complications arrive a heart attack, kidney failure, or stroke.
The underlying drivers are well understood: rapid urbanisation, changing diets, reduced physical activity, and aging populations. These trends are accelerating across Sub-Saharan Africa. The health systems built to respond were designed for infectious diseases. They were not built for chronic, longitudinal care.
The Detection Gap
The most critical failure is detection. Fewer than 20% of individuals at risk in low- and middle-income countries receive timely NCD diagnosis. This is not primarily a treatment problem it is a screening problem.
Hypertension is detectable with a blood pressure cuff. Diabetes risk can be assessed with a fasting glucose reading and a few clinical questions. Heart failure has identifiable early warning signs. These are not exotic investigations requiring specialist equipment. They are basic clinical assessments that, at scale, could shift NCD outcomes dramatically.
The problem is not technology. The problem is system design.
Rwanda's community health worker (CHW) network of 58,000 workers represents one of the most remarkable last-mile health infrastructure investments on the continent. These workers visit households, conduct maternal and child health programmes, and serve as the primary point of contact between communities and the formal health system.
They are perfectly positioned to conduct NCD screening. What they lack are the tools to do it systematically, intelligently, and in a way that feeds data into the wider health system.
Why AI Changes the Equation
Artificial intelligence does not solve the NCD crisis by itself. But it changes the equation in a specific and important way: it enables precision at scale.
A community health worker conducting a manual NCD screening can assess individual risk factors blood pressure, BMI, family history, symptoms. But without a validated model, the translation of those factors into a reliable risk score is inconsistent. The same presentation will be assessed differently by different workers, in different facilities, on different days.
AI-powered risk scoring creates consistency. When every CHW uses the same validated model, the quality of screening no longer depends on individual experience or clinical intuition. It depends on the quality of the data collected and the accuracy of the model.
This is what Sanitel does. It takes the inputs that CHWs already collect and processes them through five validated NCD screening models to generate explainable, actionable risk scores with each prediction showing the clinical factors that drove it, so the reviewing clinician can audit the AI's reasoning and make an informed decision.
What "Explainable" Means in Practice
One of the most important design decisions in Sanitel was to build explainability into every AI prediction from day one.
The temptation in clinical AI is to optimise purely for accuracy. A black-box model that achieves 95% accuracy on a test set is technically impressive. But if a clinician cannot understand why the model assigned a high-risk score to a patient, they cannot meaningfully exercise clinical judgment. They are either rubber-stamping the AI or ignoring it.
Sanitel's approach is different. Every risk score is accompanied by a ranked list of the contributing factors which biomarkers, which symptoms, which demographic variables pushed the score in which direction. A clinician reviewing a hypertension risk score can see that the patient's elevated systolic pressure, positive family history, and high sodium intake were the primary drivers. They can assess whether those factors were measured accurately, whether there are clinical factors the AI could not observe, and whether the score aligns with their clinical impression.
This is not a limitation of the AI. It is a feature. Clinical AI should augment human judgment, not replace it.
The Policy Case
Addressing Africa's NCD crisis requires action at three levels: individual early detection, facility-level systematic screening, and national population health surveillance.
Each level requires data. And currently, that data does not exist in most African health systems in a form that is actionable for policy.
Rwanda's Ministry of Health has a national Health Information System (DHIS2) that aggregates facility-level data. But the NCD data flowing into DHIS2 is incomplete, inconsistent, and retrospective. Real-time NCD surveillance knowing today, in near-real-time, what the hypertension prevalence is in a given district, or which age cohort is experiencing rising diabetes rates is not currently possible.
Sanitel's population health intelligence module is designed to change this. Every screening feeds anonymised, aggregated data into district and national dashboards in real time. For the first time, health policymakers can make NCD resource allocation decisions based on current data, not estimates from the last population survey.
A Solvable Problem
The NCD crisis in Africa is large. It is getting larger. But it is solvable with the right tools, deployed at scale, with appropriate clinical governance and ethical oversight.
Early detection is the lever. Every patient who receives a diabetes diagnosis three years before they would have otherwise has three additional years in which dietary and lifestyle intervention can prevent or delay insulin dependence. Every hypertension case caught before the first stroke saves a family from devastation and a health system from the cost of acute care.
The technology exists. The infrastructure exists. What has been missing is a platform that brings them together designed for the realities of African health systems, not imported wholesale from contexts where reliable electricity, fast internet, and abundant clinical staff can be assumed.
That is what we are building at NCD MedTech.
Christa MIKAMIRO is Chief Medical Officer at NCD MedTech Ltd and leads clinical strategy and AI governance for the Sanitel platform.