AI helps bridge global health gap as Africa leads forward

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OPINION

By Anthony Okolue

From rural Kenya to northern Nigeria, artificial intelligence is turning smartphones into medical laboratories. The implications for the continent’s healthcare gap are profound.

In 2024, a 28-year-old maize farmer in Siaya County, western Kenya, walked into a small public clinic complaining of a fever.

Ten years ago, he would have waited days – sometimes weeks – for a malaria, typhoid, or dengue diagnosis. Under a pilot that was conducted recently, a person can receive an answer in ninety seconds, since a community health worker can take a photo of a thick blood smear with an ordinary smartphone clipped to a $50 portable microscope.

An AI algorithm analyses the image and returns “Plasmodium falciparum ++” with 98.5% accuracy – better than most non-specialist lab technicians in the country.

That pilot, run by the Kenyan Ministry of Health with technical support from the startup Ubenytics, is now active in more than 420 facilities across eight counties.

Early results published in The Lancet Digital Health in March 2025 show a 31% reduction in inappropriate antibiotic prescribing and a 19% drop in severe malaria complications in intervention areas.

In northern Nigeria, the AI radiology platform Diagnosify (founded in Kano) is reading chest X-rays for tuberculosis in under 40 seconds at 27 primary-health centres in Jigawa and Sokoto states. WHO-supervised validation trials reported a sensitivity of 94.7% – higher than that of the average radiologist in many low-resource settings.

In Ghana, the mDoc telehealth platform combined with an AI triage engine reduced unnecessary emergency-room referrals from rural clinics by 43% between 2023 and 2025, according to data shared with the Ghana Health Service.

Closer to home, Rwanda’s drone-delivered blood programme now uses AI routing algorithms that have shaved average delivery time from 42 minutes to 18 minutes in hard-to-reach districts.

These are not future promises; they are documented, peer-reviewed deployments happening today.

The numbers behind the urgency are well known but worth repeating: sub-Saharan Africa has 11% of the world’s population and 24% of the global disease burden, yet only 3% of the world’s health workers and less than 1% of global health expenditure.

The specialist gap is even more stark – Nigeria, for example, has roughly one pathologist per 500,000 people against a global average of one per 25,000.

Artificial intelligence will not magically conjure more doctors, but it is already doing three things that matter:

It extends the reach of the few existing specialists. A single dermatologist in Cape Town can now review AI-flagged skin-cancer images from 60 rural clinics in the Eastern Cape in one morning – work that previously took months.

AI upgrades the accuracy of non-specialist workers as well. Community health workers using AI-assisted ultrasound in northern Uganda achieved diagnostic accuracy comparable to mid-level obstetricians for basic foetal assessment, according to a 2025 Makerere University study.

It catches diseases earlier, when they are cheaper and easier to treat. AI retinal screening for diabetic retinopathy in Zambia for instance, identified sight-threatening cases 68% earlier than the previous standard of care.

None of this is theoretical. The cost curves are collapsing faster than most policymakers realise. In 2022, training and running a high-performing malaria microscopy AI cost roughly $180,000. By late 2025, the marginal cost per test in large-scale deployments is under $0.30 – cheaper than the current rapid diagnostic test in many places once distribution and cold-chain costs are included.

The implications for Africa are asymmetric and overwhelmingly positive, provided three conditions are met:

First, regulation must keep pace. Kenya’s Pharmacy and Poisons Board and Nigeria’s NAFDAC have both issued pragmatic guidelines for AI as a medical device in the past 18 months – a quiet but crucial step that many larger economies still struggle with.

Second, local data must remain local where necessary. The most accurate algorithms for sickle-cell disease, cervical cancer pre-screening, or paediatric pneumonia in African children are being trained on African data sets.

Founders and governments that insist on data residency and local model ownership are building strategic assets, not just health tools.

Third, financing models must shift from perpetual donor pilots to sustainable  integration. Rwanda and Ghana are already bundling AI diagnostics into their national health insurance schemes. When a service is reimbursed at $1–2 per test instead of being grant-dependent, scale happens overnight.

The writer is a Nigerian-trained pharmacist, CEO of Paraclete Pharmacy & Stores, LTD in Port-Harcourt, Nigeria