Mapping AI diagnostic innovation networks in sub-Saharan Africa: a social network analysis of trials, institutions, and funders

<h2>Summary</h2><h3>Background</h3> Artificial intelligence (AI) diagnostic technologies are emerging tools for disease screening in sub-Saharan Africa, yet the partnership structures shaping their clinical validation remain uncharacterised. We conducted an exploratory network analysis to map the relational structure of AI diagnostic clinical trial networks across the region. <h3>Methods</h3> We conducted a social network analysis of all registered AI diagnostic clinical trials in sub-Saharan Africa through November 30, 2024, identified via systematic searches of ClinicalTrials.gov, PACTR, and the WHO ICTRP. We extracted institutional affiliations, funding sources, and technology partnerships from trial registries and characterised network structure using degree, betweenness, and closeness centrality. <h3>Findings</h3> We identified 11 registered trials across 10 countries involving 35 institutions (46 nodes, 97 edges; density 0.094). Trials concentrated in East Africa (36.4%) and addressed tuberculosis (27.3%), cervical cancer (27.3%), and diabetic retinopathy (18.2%). Six trials (54.5%) had completed enrolment; 72.7% were registered post-2020. Funding was diversified across US government agencies, private foundations, and Swiss institutions. Network analysis revealed 11 disconnected components with uniformly zero betweenness centrality, indicating no institution occupied a bridging position between trials. Degree centrality ranged 0.022–0.178 (mean 0.094 ± 0.051). <h3>Interpretation</h3> AI diagnostic clinical trial networks in sub-Saharan Africa exhibit complete structural fragmentation with no institutional bridging positions, suggesting limited cross-trial knowledge transfer despite active participation. This pattern likely reflects nascent field development, disease-specific siloing, and project-based funding. Structural connectivity alone cannot determine ecosystem sustainability — genuine capacity building requires regulatory approvals, local research leadership, and health system integration that network metrics cannot assess. <h3>Funding</h3> None.