Feasibility of AI-driven disease surveillance systems at international airports in sub-Saharan Africa: a narrative review

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Background: Artificial Intelligence (AI) has the potential to enhance disease surveillance, particularly at international airports, by improving the early detection and response to infectious diseases. This narrative review assesses the feasibility of implementing AI-driven disease surveillance systems at international airports in SSA. Methods: A comprehensive search of academic databases was conducted to identify relevant studies and policies. The review synthesized findings and categorized them into three thematic areas: AI effectiveness, ethical and privacy concerns, and infrastructure and capacity gaps. Results: The review suggests that implementing AI-driven disease surveillance systems at SSA international airports may be feasible in principle, although the available evidence is largely conceptual and policy-oriented rather than derived from empirical deployment at airports. Realizing this potential would likely depend on first addressing critical barriers such as inadequate data quality, insufficient infrastructure, and a shortage of trained personnel. These challenges might be mitigated through targeted investments in digital infrastructure, workforce capacity-building, and the establishment of clear regulatory frameworks to support ethical AI deployment. Conclusion: This study suggests that AI-driven disease surveillance could meaningfully strengthen public health security at international airports in Sub-Saharan Africa, provided that critical challenges in infrastructure, privacy, and regulation are addressed. The review offers a preliminary, evidence-informed framework rather than confirmation of operational feasibility. Country-specific feasibility studies and pilot implementations will be essential to test these systems under real-world conditions and to inform a possible shift toward more resilient, data-driven health infrastructures.

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