AI could close Africa’s deadly diagnosis gap for diabetes and sickle cell disease if deployed equitably

Africa accounts for the majority of global births affected by the condition, yet universal newborn screening remains rare. Without early diagnosis and preventive care, childhood mortality rates remain unacceptably high. The disease requires lifelong monitoring and management, but fragmented health services and limited specialist access leave many patients without consistent care.

Africa’s health systems are facing a dual crisis that has long remained underreported and structurally unresolved. Diabetes mellitus and sickle cell disease continue to drive preventable illness and early death across the continent, despite being conditions where early diagnosis and long-term management can dramatically reduce harm.

A newly published review shows how machine learning and deep learning could reshape detection, monitoring, and care delivery for these diseases, while warning that technology alone will not fix deep-rooted systemic gaps.

The study, titled Comparative Epidemiology of Machine and Deep Learning Diagnostics in Diabetes and Sickle Cell Disease: Africa’s Challenges, Global Non-Communicable Disease Opportunities, and published in the journal Electronics, analyzes current AI-driven approaches to diagnosing and managing diabetes and sickle cell disease across African settings, assessing both their promise and their limitations in low-resource healthcare environments.

A silent burden of late diagnosis and preventable harm

The review paints a stark picture of how diabetes and sickle cell disease continue to strain African health systems. Diabetes prevalence is rising rapidly, driven by urbanization, dietary shifts, and aging populations. Yet a significant proportion of cases remain undiagnosed until complications appear. Late detection leads to irreversible damage, including kidney failure, vision loss, nerve damage, and cardiovascular disease. Many patients first enter the health system when treatment options are already limited and costly.

Sickle cell disease presents a different but equally urgent challenge. Africa accounts for the majority of global births affected by the condition, yet universal newborn screening remains rare. Without early diagnosis and preventive care, childhood mortality rates remain unacceptably high. The disease requires lifelong monitoring and management, but fragmented health services and limited specialist access leave many patients without consistent care.

The authors argue that both diseases expose the same underlying weaknesses: limited laboratory infrastructure, poor access to specialists, lack of longitudinal patient data, and minimal population-level surveillance. In this context, artificial intelligence is increasingly viewed as a way to extend diagnostic capacity beyond traditional hospital settings and bring decision support closer to primary care and community health workers.

Across the reviewed studies, machine learning models are being applied to predict diabetes risk using routine clinical and demographic data, allowing earlier identification of high-risk individuals. Image-based deep learning systems are being tested to detect diabetic retinopathy from retinal images, offering a scalable alternative to ophthalmologist-led screening. For sickle cell disease, AI tools analyze blood smear images and clinical markers to support faster and more accurate diagnosis, even in settings without advanced laboratory equipment.

The review finds that many of these models achieve high accuracy in controlled studies. However, it also highlights that performance metrics alone do not capture the real challenge of deployment in African healthcare systems.

AI performance meets real-world constraints

Most AI models reviewed were developed using small datasets collected from single institutions, often outside Africa. This raises serious concerns about generalizability. Models trained on data from high-income countries may fail to reflect genetic diversity, environmental factors, disease presentation, and care pathways unique to African populations.

Even when models are trained locally, data quality remains a major obstacle. Inconsistent record-keeping, missing values, and lack of standardized diagnostic protocols undermine model reliability. The review notes that few studies conduct external validation across multiple regions or health systems, making it difficult to assess whether AI tools would perform safely at scale.

Infrastructure limitations further complicate adoption. Many AI systems assume stable electricity, reliable internet access, and modern computing resources. In rural and under-resourced settings, these assumptions often do not hold. The authors stress that AI tools must be designed for offline-first operation, low computational demand, and compatibility with mobile devices if they are to reach frontline care environments.

Integration into clinical workflows is another persistent gap. The review finds that many AI tools operate as standalone research prototypes rather than being embedded into routine care processes. Without alignment with how clinicians and community health workers actually operate, even highly accurate systems risk being ignored or misused.

For diabetes management, the review highlights growing interest in wearable sensors and mobile health platforms that use AI to track glucose levels, physical activity, and lifestyle factors. These tools could support continuous monitoring and early intervention, but their effectiveness depends on affordability, patient adherence, and digital literacy. For sickle cell disease, long-term monitoring tools remain scarce, reflecting broader neglect of chronic genetic conditions in health technology development.

Privacy and data governance concerns also loom large. As AI systems rely on sensitive health data, weak regulatory frameworks increase the risk of misuse, data leakage, and loss of patient trust. The authors argue that privacy-preserving approaches such as federated learning could help address these risks by allowing models to be trained without centralizing patient data, but such methods remain underexplored in African contexts.

Beyond algorithms: equity, governance, and system design

Artificial intelligence should not be treated as a standalone technological fix, the study asserts. Instead, it must be understood as part of a broader health system intervention. Without parallel investments in governance, workforce training, and infrastructure, AI risks deepening existing inequalities rather than reducing them.

Bias emerges as a critical concern. Models trained on incomplete or unrepresentative data may systematically underperform for certain populations, leading to missed diagnoses or inappropriate risk assessments. In regions already facing barriers to care, such failures could reinforce disparities rather than improve outcomes. The authors call for equity-focused evaluation that explicitly tests model performance across different demographic and socioeconomic groups.

Human oversight is another non-negotiable requirement. The review stresses that AI systems should support, not replace, clinical judgment. Overreliance on automated outputs could lead to errors being propagated without challenge, particularly in settings where clinicians are overworked or undertrained in AI interpretation. Clear guidelines are needed to define responsibility when AI-supported decisions contribute to patient harm.

The authors also highlight the importance of policy alignment. National health strategies must incorporate AI governance frameworks that address validation standards, accountability, data protection, and long-term sustainability. Without clear policy direction, AI adoption risks being driven by fragmented pilot projects rather than coordinated public health priorities.

Capacity building remains one of the most significant barriers. The review notes a shortage of professionals with combined expertise in healthcare, data science, and AI across Africa. Addressing this gap will require sustained investment in education, interdisciplinary collaboration, and local research capacity. Imported solutions without local ownership are unlikely to endure or adapt to changing needs.

Despite these challenges, the authors remain cautiously optimistic. They argue that when designed with local realities in mind, AI tools could help shift diabetes and sickle cell disease care from reactive treatment to proactive management. Early risk prediction, accessible screening, and continuous monitoring could reduce long-term costs and prevent avoidable suffering.