ReviewFrontiers in public health2026
Feasibility of AI-driven disease surveillance systems at international airports in sub-Saharan Africa: a narrative review.
Review in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.