Beyond Pilot Deployments: Why AI Imaging Diagnostics in Africa Needs Shared Data Infrastructure, Not More One-Off Models

Artificial intelligence for medical imaging has produced a growing number of promising pilot deployments across Africa, from chest radiograph analysis for tuberculosis and pneumonia to AI-assisted obstetric ultrasound and retinal screening. However, these successes remain largely isolated: single institution models trained on narrow local datasets, rarely validated across sites, and almost never interoperable with one another. Recent systematic reviews of AI in African healthcare have cataloged this fragmentation at a broad level, but do not propose an operational road-map specific to imaging diagnostics [Alaran et al., 2025, Ibinaiye et al., 2025]. We argue that the central obstacle to scaling AI imaging diagnostics across the continent is not a shortage of algorithmic capability, but the absence of shared infrastructure: annotated, protocol diverse datasets; validation standards suited to low-resource deployment; and model architectures designed for the compute constraints of district-level facilities. Drawing on evidence from recent clinical validation studies, generalizability studies, and diagnostic accuracy meta-analyses, we characterize three structural barriers poor cross-site generalizability, data and annotation scarcity, and a widening adoption gap between teaching and district hospitals and show how each is documented in isolation across the recent literature but rarely synthesized into a single, actionable agenda. We then propose a concrete roadmap centered on multi country data consortiums, low-compute model design, and LMIC specific validation standards, closing the gap between diagnosis of the problem and a plan to act on it. We argue that solving these problems in the African context yields lessons directly transferable to other resource-constrained health systems globally.