Template-Type: ReDIF-Article 1.0 Author-Name:Selorm Kweku Dzokoto Author-Workplace-Name:PhD Researcher (School of Information and Software Engineering), University of Electronic Science and Technology of China, Sichuan P.R., 611731 Chengdu, China. Author-Name: Saio Alusine Marrah Author-Workplace-Name:PhD Researcher (School of Information and Software Engineering), University of Electronic Science and Technology of China, Sichuan P.R., 611731 Chengdu, China. Author-Name: Sofo Tanko Rashid Computer Author-Workplace-Name:PhD Researcher, Department of Humanities, Accra Metropolitan University, Accra, Ghana. Author-Name: Karl Mark Arhin Author-Workplace-Name:Doctor of Philosophy (Political Science Department), Kaaf University, Kasoa, Ghana. Author-Name: Frank Korbla Dzokoto Author-Workplace-Name:Associate Dean (Pearson Construction Management Faculty), Global Banking School, U.K. Author-Name: Shahid Nawaz Author-Workplace-Name:Masters’ Student (School of Information and Software Engineering), University of Electronic Science and Technology of China, Sichuan P.R., 611731 Chengdu, China. Author-Name: Emefa Arisiya Author-Workplace-Name:Health Professional, University of Wisconsin, United States of America. Title:From Policy to Practice: A Governance Framework for Trustworthy Artificial Intelligence in Medical Imaging in Low and Middle-Income Countries: Ghana as an Anchoring Case Abstract:Artificial intelligence (AI) is transforming medical imaging by improving diagnostic accuracy, streamlining workflows, and expanding access to specialist interpretation. These advances hold particular promise for low- and middle-income countries (LMICs) such as Ghana, where severe shortages of radiologists constrain timely diagnosis. Yet the introduction of AI also introduces substantial risks related to algorithmic bias, weak data governance, cybersecurity vulnerabilities, unclear clinical accountability, and dependence on external vendors. International guidance on trustworthy AI, while valuable, frequently presupposes institutional capabilities that many LMICs do not yet possess, creating a persistent policy-to-practice gap. This conceptual framework paper addresses that gap by proposing a seven-pillar governance framework for trustworthy AI in medical imaging, anchored in the Ghanaian context and designed for transferability across LMICs. The pillars comprise: (1) Governance and Leadership, (2) Ethical and Legal Compliance, (3) Data Governance and Security, (4) Clinical Validation and Safety, (5) Human Capacity Development, (6) Infrastructure and Digital Readiness, and (7) Monitoring, Auditing, and Continuous Improvement. The framework is operationalized through a five-level governance maturity model, measurable indicators, and a phased implementation roadmap. Drawing on international instruments (WHO, IMDRF, OECD, FUTURE-AI), comparative experiences from Rwanda, Kenya, Nigeria, South Africa, India, and other LMICs, and insights from institutional and technology governance theory, the paper translates high-level principles into practical institutional machinery. The contribution is both theoretical linking health policy, institutional, and technology governance perspectives and practical, offering decision-makers a capacity-proportionate pathway from aspirational policy to accountable practice. Keywords:Artificial intelligence, Medical imaging governance, Trustworthy AI, Low- and middle-income countries, Ghana, Health policy, Conceptual framework Journal: Journal of Scientific Reports Pages:36-51 Volume: 15 Issue: 1 Year: 2026 DOI:10.58970/JSR.1238 File-URL: https://ijsab.com/wp-content/uploads/1238.pdf File-Format: Application/pdf File-URL: https://www.ijsab.com/jsr-volume-15-issue-1/9180 File-Format: text/html Handle: RePEc:aif:report:v:15:y:2026:i:1:p:36-51