Filling the gap: artificial intelligence-driven one health integration to strengthen pandemic preparedness in resource-limited settings
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View abstract on PubMed
Summary
This summary is machine-generated.Artificial intelligence (AI) can enhance pandemic surveillance in low- and middle-income countries (LMICs) by integrating human, animal, and environmental health data. Implementing AI requires addressing resource limitations and building local capacity for equitable global health security.
Area Of Science
- One Health
- Epidemiology
- Artificial Intelligence
Background
- Emerging zoonotic diseases underscore the need for integrated human, animal, and environmental health surveillance.
- Low- and middle-income countries (LMICs) face significant challenges in pandemic preparedness due to limited resources and fragmented data systems.
- Current artificial intelligence (AI) tools are often domain-specific and designed for high-income settings, limiting their applicability in LMICs.
Purpose Of The Study
- To review the potential of AI in addressing One Health integration gaps for pandemic surveillance in LMICs.
- To propose an AI-driven One Health framework tailored for resource-limited settings.
- To identify key components for successful AI implementation in LMIC pandemic preparedness.
Main Methods
- Literature review of AI applications in One Health and pandemic surveillance.
- Development of an AI-driven One Health framework for LMICs.
- Description of a dual solution combining Graph Neural Networks and offline-first mobile applications.
Main Results
- AI offers potential for automated data harmonization, predictive modeling, and resource optimization in pandemic surveillance.
- Successful AI implementation in LMICs necessitates addressing barriers like limited resources, fragmented data, and the need for digitization.
- A proposed framework emphasizes resource optimization, governance, collaboration, capacity building, and health system integration.
Conclusions
- AI technologies hold significant promise for enhancing pandemic preparedness in LMICs.
- Equitable AI-based surveillance systems require prioritizing mobile solutions, community engagement, digitization, cultural adaptation, and local capacity building.
- An integrated AI-driven One Health approach is crucial for strengthening global health security.
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