AI Applications for Epidemic Preparedness in Cross-Border Contexts (IHR Perspective)
1
Public Health in Emergencies and Disasters Working Group - WFPHA, Nablus - P400, Palestinian Territory, Occupied
Popul. Med. 2026;8(Supplement Supplement 1):A959
ABSTRACT
INTRODUCTION:
AI-powered tools such as natural language processing (NLP), predictive analytics, and open-source intelligence (OSINT) are reshaping epidemic surveillance and response. Yet their integration into International Health Regulations (IHR) core capacities remains inconsistent—especially across borders.
METHODS:
This oral session will present synthesized evidence on AI use in cross-border preparedness, drawing from pilot projects, database reviews, and regional experiences in Africa, Asia, and Europe. A draft roadmap will be shared to support national and regional implementation aligned with IHR.
RESULTS:
The session will explore use cases, barriers, and ethical considerations around AI integration in LMICs. It will also present frameworks for equitable digital adoption, and recommendations for regional epidemic intelligence systems.
CONCLUSIONS:
AI must serve global health equity—not widen the gap. Cross-border preparedness needs intelligent, inclusive, and interoperable solutions to meet the challenges of tomorrow.
AI-powered tools such as natural language processing (NLP), predictive analytics, and open-source intelligence (OSINT) are reshaping epidemic surveillance and response. Yet their integration into International Health Regulations (IHR) core capacities remains inconsistent—especially across borders.
METHODS:
This oral session will present synthesized evidence on AI use in cross-border preparedness, drawing from pilot projects, database reviews, and regional experiences in Africa, Asia, and Europe. A draft roadmap will be shared to support national and regional implementation aligned with IHR.
RESULTS:
The session will explore use cases, barriers, and ethical considerations around AI integration in LMICs. It will also present frameworks for equitable digital adoption, and recommendations for regional epidemic intelligence systems.
CONCLUSIONS:
AI must serve global health equity—not widen the gap. Cross-border preparedness needs intelligent, inclusive, and interoperable solutions to meet the challenges of tomorrow.
| eISSN: | 2654-1459 |
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