AI-Powered Diagnostics in Low-Resource Settings: What Works in Central Africa

The Promise and the Reality

AI-powered diagnostic tools have been among the most discussed innovations in global health over the past decade. The promise is compelling: algorithms that can read chest X-rays for tuberculosis, identify malaria parasites in blood films, detect diabetic retinopathy in fundoscopic images, and screen for cervical cancer in colposcopy images — potentially replacing the specialist expertise that low-resource settings lack.

The reality in 2025 is more nuanced. Some AI diagnostic applications are genuinely ready for real-world deployment in Central Africa. Others remain in research or early pilot phases, with important questions about performance in the local context unanswered. And all of them share a dependency on digital infrastructure — patient records, image management systems, connectivity — that most facilities in the CEMAC region are still building.

This article provides an honest assessment: what AI diagnostics actually work in the Central African context in 2025, what the prerequisites are, and what health facilities should realistically plan for.

What "AI Diagnostics" Actually Means

Artificial intelligence diagnostic tools in healthcare use machine learning algorithms — trained on large datasets of images, clinical records, or biosignals — to identify patterns associated with specific diseases or conditions.

There are two main types relevant to the African context:

Image-based AI diagnostics: Algorithms that analyse medical images — chest X-rays, fundoscopic photographs, skin photographs, blood film microscopy images — to detect pathology. These are the most mature AI diagnostic applications and the ones with the strongest evidence base in low-resource settings.

Clinical data-based AI diagnostics: Algorithms that analyse structured clinical data — symptoms, vital signs, laboratory results, patient history — to suggest diagnoses or identify high-risk patients. These require clean structured data and are less mature than image-based tools in the African context.

What Works: Validated AI Diagnostic Applications for Central Africa

Tuberculosis Detection from Chest X-Rays

TB remains one of the highest-burden diseases in the CEMAC region. WHO estimates suggest that Central Africa has some of the highest TB prevalence rates globally, with significant rates of undiagnosed infection.

AI-powered chest X-ray analysis for TB screening is the most validated AI diagnostic application in low-resource settings globally. Tools including CAD4TB, qXR, and Lunit INSIGHT have been validated in large African populations and approved by WHO as a triage tool for TB.

In settings where a radiologist is unavailable, AI chest X-ray analysis provides a tool that can triage X-ray findings with sensitivity comparable to human radiologists — identifying which patients need further testing and which can be safely classified as low-risk.

Current deployment in CEMAC: Pilot programmes in Cameroon and neighbouring countries, primarily through NGO-led TB programmes, have deployed CAD4TB and similar tools. Wider deployment is constrained by X-ray equipment availability and the need for digital imaging infrastructure.

Prerequisite for deployment: Digital X-ray equipment (or digitised films), an image management system, and internet connectivity for cloud-based AI analysis (or local deployment for offline settings).

Malaria Diagnosis from Blood Films

Manual microscopy for malaria parasite identification requires trained microscopists — a cadre that is in short supply in many district health facilities in Central Africa. Rapid diagnostic tests (RDTs) are widely available but have limitations in sensitivity and species identification.

AI-powered blood film analysis — using smartphone-attached microscopes and image analysis algorithms — has been validated in several African countries as a tool that can achieve microscopist-level accuracy without requiring specialist training.

Tools including EasyScan GO and the AI-powered platforms from startups including Portable Genomics have demonstrated performance in Cameroonian and CEMAC settings.

Current deployment: Still primarily in research and pilot phases in CEMAC, but moving toward wider deployment through health system partnerships.

Prerequisite for deployment: Smartphone-compatible microscope attachment (relatively low cost), smartphone or tablet, and training in the image capture procedure.

Diabetic Retinopathy Screening

Diabetes prevalence in urban Cameroon is increasing rapidly — estimated to affect 6–10% of the urban adult population. Diabetic retinopathy is the most common cause of preventable blindness in people with diabetes, and early detection dramatically reduces the risk of vision loss.

AI fundoscopic analysis — using a digital fundus camera to capture retinal images and analysing them with a validated algorithm — can screen for diabetic retinopathy with sensitivity and specificity comparable to ophthalmologist review, in settings where ophthalmologists are not available.

Google's retinal AI, Microsoft's RetinalAI, and several specialised platforms have been validated in African contexts and are moving toward deployment in specific diabetes management programmes.

Current deployment: Available in some diabetes specialist programmes in major Cameroonian cities. District-level deployment is constrained by fundus camera availability and cost.

Cervical Cancer Screening

Cervical cancer is a leading cause of cancer mortality among women in Cameroon. AI-powered visual inspection of the cervix — using a smartphone camera and an analysis algorithm — has been validated as a screening tool that can identify precancerous lesions with performance approaching colposcopy specialist review.

This application is particularly relevant for settings where specialist gynaecology is unavailable, as it can be administered by trained nurses or community health workers.

What Does Not Yet Work Reliably: Honest Assessment

Several AI diagnostic applications that receive significant attention have limited proven applicability in the Central African context in 2025:

AI symptom checkers: Apps that collect patient-reported symptoms and suggest diagnoses perform poorly when trained primarily on patient populations from high-income countries. Disease prevalence, symptom presentation patterns, and the range of conditions present in Cameroonian patients are systematically different. Symptom checkers developed for European or American populations should not be used clinically in the Cameroonian context without local validation.

AI-assisted ECG interpretation: Algorithms for detecting cardiac arrhythmias and ischaemia from ECG data are well-validated in high-income country populations. Their performance in African populations — where specific genetic factors influence cardiac electrical patterns and specific cardiomyopathies are more prevalent — requires local validation that is still limited.

AI radiology beyond chest X-ray: AI analysis of CT scans, MRIs, and ultrasound for conditions beyond pulmonary pathology requires both validated algorithms and the imaging equipment that generates the input data. CT and MRI availability in CEMAC remains limited to tertiary facilities.

"General purpose" AI diagnosis: Any claim that an AI system can provide general diagnosis across a wide range of conditions in a Cameroonian context should be treated with significant scepticism. AI diagnostic tools are validated for specific diseases and specific image types, not as general diagnosticians.

The Infrastructure Prerequisite: Why Digital Health Comes First

Every AI diagnostic tool described above requires supporting digital infrastructure:

• Digital imaging systems (for image-based AI) or structured digital clinical data (for data-based AI)
• Connectivity for cloud-based analysis, or local deployment capability
• Integration with the patient record system, so AI results appear in the clinical record
• Clinical workflows that incorporate AI output into the decision-making process

This infrastructure — specifically the integrated hospital management system that creates and maintains digital patient records — is the prerequisite for all AI applications. Facilities that are still operating on paper cannot benefit from AI diagnostic tools, regardless of how good those tools become.

The investment in digital transformation is therefore simultaneously an investment in AI readiness. Every patient record created in a structured digital system is a record that can, in future, be analysed algorithmically. Every digital image stored in a connected imaging system is an image that can be processed by an AI diagnostic tool.

Frequently Asked Questions

Can AI diagnose diseases as accurately as a doctor? For specific, well-defined image interpretation tasks (TB on chest X-ray, retinopathy on fundoscopic photo, malaria on blood film image), validated AI tools have demonstrated accuracy comparable to specialist clinicians in controlled studies. In real-world deployment, performance varies with image quality, population characteristics, and implementation quality. AI in these applications is best understood as a decision support tool, not a replacement for clinical judgment.

Are AI diagnostic tools approved by regulatory bodies for use in Cameroon? Regulatory approval of medical AI tools in Cameroon is still developing. Tools approved by WHO or CE-marked in Europe have been deployed in donor-funded programmes. Facilities deploying AI tools should confirm their regulatory status and seek guidance from the Ministry of Public Health where required.

How much do AI diagnostic tools cost? Cost varies widely. Some tools (particularly those deployed through global health programmes like CAD4TB) are available at low or no cost for public health facilities. Commercial tools vary from per-analysis fees (a few hundred XAF per image processed) to subscription models. Hardware requirements (digital fundus cameras, microscope attachments) carry one-time purchase costs that are the primary barrier for many facilities.

Conclusion: Selective, Evidence-Grounded AI Deployment

AI-powered diagnostics in Central Africa in 2025 is a story of selective maturity — a small number of well-validated applications (TB screening, malaria diagnosis, retinopathy screening) ready for deployment, and a much larger landscape of promising but unvalidated applications that should be approached critically.

For health facility administrators in Cameroon, the priority is:

1. Build the digital health infrastructure that AI requires
2. Monitor validated AI diagnostic tools in specific disease areas
3. Engage with implementation programmes when they become available in your specialty and geography
4. Maintain appropriate scepticism toward AI claims that are not supported by local validation evidence

The AI diagnostic revolution in Central Africa is coming. The facilities that will benefit most are those that have already built the digital foundation it requires.

OPES Health Systems provides the digital health management infrastructure that enables AI diagnostic tool integration for hospitals and clinics across Cameroon and the CEMAC region.