INTRODUCTION:
Artificial intelligence is increasingly layered onto digital-health systems in low-middle-income countries( LMICs), often before the underlying foundations of governance, infrastructure, and accountability are fully established. Ghana’s Lightwave-Health-Management-System(LHIMS) was introduced to strengthen health data management, but its rollout exposed critical weaknesses in interoperability, vendor dependence, and institutional oversight. The governance weakness displayed by LHIMs raises questions about AI readiness and risk in LMICs.

METHODS:
A qualitative policy and systems analysis was conducted using national digital policy documents, LHIMS implementation reports, stakeholder and practitioner perspectives from roll-out facilities. A thematic analysis was conducted across key governance domains: accountability, data stewardship, vendor-dependence and ethical oversight.

RESULTS:
The analysis identifies significant governance weakness in LHIMs implementation. Limited interoperability and closed system architecture constrained data sharing across facilities and levels of care, while vendor lock-in reduced institutional control over public health data and system evolution. Accountability for data stewardship, system performance and ethical oversight remained insufficiently defined. Finally, AI-specific governance considerations- such as algorithmic transparency, auditability and bias mitigation were largely absent from the digital health framework, raising major concerns about equitable and trustworthy AI deployment.

CONCLUSIONS:
The LHIMS-experience demonstrates that digital health systems without robust governance constitutes a fragile foundation for AI in public health and increase the bias gap and risks. For LMICs, governance readiness is a prerequisite for ensuring that AI strengthens health systems resilience, equity, and public trust rather than exacerbating existing vunerabilities. Ghana's experience offers transferable lessons for global policies , underscoring the need to align digital transformation with people centered, ethically-grounded AI governance.

RECOMMENDATIONS:
-Make governance a precondition for AI and digital health deployment -Establish national digital and AI governance bodies. -Contracts should explicitly guarantee state ownership, portability and reuse of public-health data. -Invest in Public-Sector Technical Sovreignty -Integrate equity and bias audit into routine practice.