Challenges and Opportunities of AI-Assisted Diagnostics for Malaria and Tuberculosis in Africa

Malaria and tuberculosis remain among the most serious infectious diseases in Africa. Sub-Saharan Africa carries roughly 95% of the global malaria burden, and the continent accounts for close to a quarter of tuberculosis cases, with HIV co-infection making diagnosis and treatment harder. Care is hampered by a severe shortage of trained staff: many countries have fewer than 1 radiologist per million people, and skilled microscopists are scarce in rural and peri-urban facilities. This review asks whether artificial intelligence, and convolutional neural networks in particular, can close that gap by automating the reading of microscopy blood smears and chest X-rays. We cover the main architectures, including YOLOv5 and Faster R-CNN for malaria and computer-aided detection tools for tuberculosis screening, and compare their performance with that of human experts. In controlled settings, these systems often match expert accuracy, as demonstrated by a model trained on Nigerian thick films, which reported a sensitivity of 0.92 and a specificity of 0.90. Moving from the lab to the clinic is the harder part. Unreliable power and internet connections, fragmented regulation, and the scarcity of locally collected training data all get in the way, and models trained on foreign datasets can lose 5 to 15% of their accuracy when validated in African settings. We argue that turning AI into a working tool for African public health requires designing for offline-first computing, protecting patient data, and moving away from funding that depends on short-term donor cycles.