Emerging technologies in medical physics: the role of artificial intelligence in medical imaging and potential adoption in Ghana.
Authors
Affiliations (6)
Affiliations (6)
- Radiological and Medical Sciences Research Institute, Ghana Atomic Energy Commission, Accra, Ghana; Medical Physics Department, School of Nuclear and Allied Sciences, University of Ghana, Accra, Ghana. Electronic address: [email protected].
- Radiological and Medical Sciences Research Institute, Ghana Atomic Energy Commission, Accra, Ghana; Medical Physics Department, School of Nuclear and Allied Sciences, University of Ghana, Accra, Ghana.
- Radiological and Medical Sciences Research Institute, Ghana Atomic Energy Commission, Accra, Ghana.
- Nuclear Regulatory Authority, Accra, Ghana.
- Medical Physics Department, School of Nuclear and Allied Sciences, University of Ghana, Accra, Ghana; Radiation Protection Institute, Ghana Atomic Energy Commission, Accra, Ghana.
- Radiation Protection Institute, Ghana Atomic Energy Commission, Accra, Ghana.
Abstract
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. Assess Ghana's readiness to adopt AI in medical imaging, identify pathways and barriers, and propose a phased, context-specific roadmap. 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. 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. 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.