Background The rapid adoption of artificial intelligence (AI) in African healthcare presents both transformative opportunities and structural risks. While AI-enabled diagnostic technologies may expand clinical capacity and improve service delivery, their deployment also raises questions of data sovereignty, infrastructural dependency, regulatory auditability, and unequal value capture. Methods This exploratory multi-case analysis examines four African health technology firms: Helium Health, Ubenwa, Neural Labs Africa, and Envisionit Deep AI. The study draws on publicly available secondary sources published between 2017 and 2025, comprising 26 peer-reviewed or academic sources, 6 policy and regulatory documents, and 4 firm-level sources. A thematic analysis was conducted through a political-economy framework using deductive and inductive coding. Results Publicly available documentation suggests three recurring structural patterns. First, the selected firms rely on AWS or Google Cloud for key infrastructure functions, creating potential limits on domestic regulatory oversight of diagnostic data pipelines and model updates. Second, the absence of publicly documented locally governed datasets and systematic local retraining points to data sovereignty and clinical validation gaps. Third, fragmented data protection regimes across Nigeria, Kenya, and South Africa create governance asymmetry that may constrain the cross-border data pooling needed for African AI model development. Conclusions Diagnostic equity in African health AI requires governance over data, infrastructure, models, and validation systems. The study proposes tiered data sovereignty frameworks, regionally coordinated health data trusts, and staged AI validation sandboxes as practical pathways for reducing dependency while preserving innovation capacity. Findings are exploratory and should be interpreted in light of reliance on publicly available evidence.