Artificial Intelligence as a Radiology Gap-Filler in Somalia: A Narrative Review with a Pragmatic Implementation Framework.

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Abstract

Diagnostic imaging supports emergency care, trauma assessment, tuberculosis detection, maternal and neonatal care, cancer diagnosis, and surgical decision-making, yet timely specialist interpretation remains limited in many low-resource health systems. Somalia is rebuilding a health system affected by workforce shortages, fragmented information systems, uneven service distribution, infrastructure constraints, and limited regulatory capacity. This narrative review integrates peer-reviewed evidence, global guidance, and Somalia-specific policy sources to examine where artificial intelligence could support radiology and to translate that evidence into a pragmatic implementation framework. The Somalia-specific contribution is a context-sensitive mapping of priority use cases, implementation barriers, governance safeguards, and clinical workflow requirements. Priority applications include computer-aided chest X-ray screening for tuberculosis, emergency imaging triage, image quality assessment, structured reporting support, and teleradiology prioritization. The proposed framework links readiness assessment, priority use-case selection, local validation, supervised deployment, workflow integration, continuous monitoring, and cautious scale-up. It also assigns human accountability at each stage and distinguishes pre-deployment readiness from clinical and operational risks during use. Artificial intelligence may act as a human-supervised capacity multiplier, but poor local validation, weak data protection, automation bias, workflow failure, and vendor dependency can undermine safety and equity. Somalia should therefore pursue leadership-led, locally validated, and auditable implementation that strengthens existing clinical services rather than introducing isolated technology.

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