AI disease surveillance at African airports feasible in principle but lacks empirical evidenceAI disease surveillance at airports: feasible in theory

Frontiers in Medicine Published July 7, 2026 DOI ↗ Editorial oversight: Dr. Sofia Müller, MD · Lifespan & Whole-Person Care

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Key Takeaway

Interpret AI surveillance feasibility as conceptual; pilot implementations are needed before operational use.

This narrative review examines the feasibility of implementing AI-driven disease surveillance systems at international airports in sub-Saharan Africa. The authors synthesized conceptual and policy-oriented literature, noting that while such systems are feasible in principle, empirical evidence from actual deployments is lacking.

Key findings highlight several barriers: inadequate data quality, insufficient infrastructure, and a shortage of trained personnel. The review also addresses ethical and privacy concerns, as well as infrastructure and capacity gaps. No pooled effect sizes are reported, as the evidence is qualitative.

The authors acknowledge limitations, including that the evidence is largely conceptual rather than derived from empirical deployment, and there is a lack of country-specific feasibility studies and pilot implementations. The certainty note emphasizes that results are not confirmation of operational feasibility but a conceptual assessment.

Practice relevance is preliminary: the review provides a framework for strengthening public health security at these airports, but real-world conditions require pilot testing before any clinical or public health recommendations can be made.

A new review looked at whether artificial intelligence (AI) could help detect disease outbreaks at international airports in sub-Saharan Africa. The idea is to use AI systems to monitor travelers and spot health threats early. But the review found that while this approach seems possible in theory, there is no real-world evidence yet that it works in practice.

The evidence comes from policy papers and conceptual studies, not from actual AI systems deployed at airports. The review also identified major barriers: poor data quality, lack of infrastructure, and not enough trained staff. These challenges would need to be solved before AI could be used effectively.

No safety issues were reported because no actual AI system was tested. The review is a first step, not a proof that AI works. The authors stress that pilot projects are needed to see if AI can really help in real airport settings.

For now, this is a promising idea that needs more testing. Readers should not assume AI is ready for use at airports. More research and pilot programs are needed to turn this concept into reality.

What this means for you:

AI surveillance at airports is promising in theory but needs real-world testing.

Common questions

Is AI disease surveillance at airports proven to work?

No, the review says it is feasible in principle but lacks evidence from actual airport use. The findings are based on conceptual studies, not real-world deployment.

What are the main barriers to using AI at airports?

The review found three main barriers: inadequate data quality, insufficient infrastructure, and a shortage of trained personnel. These need to be addressed before AI can be used.

Who would benefit from AI disease surveillance at airports?

The review focuses on international airports in sub-Saharan Africa. If proven effective, AI could help public health officials detect and respond to disease outbreaks more quickly.

Study Details

Study typeSystematic review

EvidenceLevel 1

PublishedJul 2026

View Original Abstract ↓

BackgroundArtificial 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.MethodsA 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.ResultsThe 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.ConclusionThis 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.

AI disease surveillance at African airports feasible in principle but lacks empirical evidenceAI disease surveillance at airports: feasible in theory

Common questions

Study Details

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AI disease surveillance at African airports feasible in principle but lacks empirical evidenceAI disease surveillance at airports: feasible in theory

Common questions

Study Details

Primary community health care nurses improve patient outcomes and reduce costs compared to medical practitioners

Meta-analysis shows primary care nurses improve health and save money compared to medical practitioners

Clinical research that matters. Delivered to your inbox.

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