Benin’s malaria program just got an AI upgrade from Seoul 🇧🇯🧬

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Benin’s national malaria control program (PNLP) has selected an AI-powered diagnostic platform, miLab MAL, built by South Korean company Noul, through a public tender. The tool is now an official diagnostic instrument in the country’s malaria program, alongside existing methods.

What the machine actually does ⚙️

miLab MAL scans and reads blood smears without direct human interpretation. The process runs through sample preparation, digital imaging, and algorithmic analysis, with a stated capacity — according to the manufacturer — to examine up to 300,000 red blood cells per test in search of parasites or malaria-related hemorrhaging. That sets it apart from manual microscopy, where results hinge on a technician’s experience, and from standard rapid tests, which are less reliable at detecting low parasite loads.

The numbers behind Benin’s clinical evaluation 📊

Before joining the national program, the device was tested in 2025 on 211 children suspected of severe malaria, at university hospitals in the north and south of the country, under government oversight. Noul reports a sensitivity of 98.82% and a specificity of 100% for that study, compared with manual microscopy as the reference standard, along with an analysis time cut to 20 minutes, versus an average of 75 minutes for the manual method. Based on those results, Benin’s PNLP classified the device as a point-of-care diagnostic tool and recommended its integration into malaria control strategies across the continent.

A controlled rollout 📡

Under the contract, a first batch of 20 units will go to hospitals handling severe cases, ahead of a gradual expansion to other public facilities. Benin — where the entire population lives in a malaria-endemic zone — reportedly recorded around 5.1 million cases and nearly 9,900 deaths in 2024, according to WHO estimates cited in the file. Noul, whose technology reportedly already equips institutions in some 30 countries, is presenting the deal as a foothold for expanding into Africa’s public health market, according to statements from CEO Lim Chan-yang.

This deal fits a broader pattern: automated diagnosis as one possible answer to the chronic shortage of trained medical staff across several African health systems. What remains to be seen is whether the technology — for now limited to reference hospitals — can reach further, into the smaller, harder-to-reach clinics where that shortage is often felt most.

So, can AI meaningfully close the gap left by a lack of qualified health technicians across Africa? Tell us what you think in the comments.