Mark Pokoo-Aikins, Francis Hasford, Theresa Bebaaku Dery, Edem Kwabla Sosu, Theophilus Akumea Sackey, Linda Osei Poku, Shiraz Issahaku, Mary Boadu, Annette Agyabeng, Lawrence Akonor Sarsah, Belinda Buermle Asamanyuah, Kofi Okyere Akyea-Larbi, Lordina Owusuwaa Asibey, Bismark Djan
AI can improve equity and efficiency in imaging in Ghana if adoption builds on strong digital foundations, robust governance, local validation, and clinician-led implementation. Priorities include PACS deployment, AI-specific regulatory strengthening, ethical data governance, and capacity building to support safe, equitable, and sustainable use.
BACKGROUND: Ghana's imaging services face rising demand, uneven digital infrastructure, and limited access to advanced modalities. Artificial intelligence (AI) could improve diagnostic accuracy, workflow efficiency, and access, but real-world adoption is early.
OBJECTIVE: Assess Ghana's readiness to adopt AI in medical imaging, identify pathways and barriers, and propose a phased, context-specific roadmap.
METHODS: A review of peer-reviewed and grey literature (2012-March 2025) using PubMed, IEEE Xplore, Scopus, Google Scholar, and Ghanaian institutional documents. The review emphasizes imaging AI, LMIC experiences, and Ghana-specific evidence on infrastructure, policy, and pilots, distinguishing Ghana-based findings from international evidence extrapolated to Ghana.
RESULTS: Major gaps include digital infrastructure (limited PACS, variable DR/CR adoption, uneven connectivity), financing (license and maintenance costs), governance (SaMD pathways exist but AI-specific provisions are evolving; operational data protection needs strengthening), and workforce (limited AI literacy; urban - rural disparities). Ghana-relevant touchpoints include MinoHealth.AI chest radiography evaluations, the national imaging equipment inventory, Ghana Health Service digital health strategy (2023-2027), and FDA SaMD guidance. A phased roadmap is proposed: establish PACS and connectivity; implement AI governance and data stewardship; run targeted pilots in CXR triage, low-dose CT, and MRI acceleration; scale via public-private partnerships and pooled procurement; and sustain workforce development with human-in-the-loop oversight.
CONCLUSIONS: AI can improve equity and efficiency in imaging in Ghana if adoption builds on strong digital foundations, robust governance, local validation, and clinician-led implementation. Priorities include PACS deployment, AI-specific regulatory strengthening, ethical data governance, and capacity building to support safe, equitable, and sustainable use.