Artificial Intelligence - Clinical Decision Support Systems: From brain drain to health system gain

Background: The persistent migration of healthcare professionals from low- and middle-income countries (LMICs) to high-income regions, commonly termed brain drain, has deepened workforce shortages and weakened clinical decision capacity, especially across Africa. Artificial Intelligence–driven Clinical Decision Support Systems (AI-CDSS) offer a potential countermeasure by augmenting clinicians, standardizing care, and redistributing expertise through digital and diaspora-linked networks. Objective: This narrative review examines the role of AI-CDSS in mitigating workforce deficits, enhancing diagnostic capacity, and strengthening healthcare system resilience, with Africa positioned as both a stress test and innovation testbed for equitable AI deployment. Methods: A structured literature review was conducted across PubMed, Scopus, IEEE Xplore, and Web of Science, covering studies published between 2015 and 2025. Eligible publications addressed AI-CDSS applications linked to clinical decision-making and workforce support. Evidence was synthesized thematically under five domains: capabilities, workforce implications, evidence validation, barriers, and governance. Results: AI-CDSS applications demonstrated notable potential in triage, diagnostics, and clinical decision standardization. Evidence from LMICs revealed feasibility of offline-first, edge-AI, and federated learning deployments despite infrastructural constraints. However, empirical validation remains limited, with few prospective evaluations and minimal African data representation. Ethical governance and human–AI trust emerged as decisive enablers of sustainable adoption. Conclusions: AI-CDSS can transform healthcare workforce shortages into opportunities for systemic resilience by enabling brain circulation, linking diaspora expertise with local practitioners through AI-assisted workflows. Building inclusive, African-led AI ecosystems is essential to ensure that future health innovations are both technically rigorous and socially equitable.