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Artificial Intelligence for Public Health Surveillance: Promise, Inequities, and the Need for Transparent Global
1Section of Emergency Medicine, Department of Pediatrics, Yale School of Medicine, New Haven, CT, USA.
Abstract:
Artificial intelligence (AI) has demonstrated a strong technical potential to enhance public health surveillance, but routine implementation remains limited. Most AI surveillance systems are developed in high-income settings, reducing generalizability and limiting usefulness in low- and middle-income countries. Barriers to adoption include data inequities, weak digital infrastructure, limited workforce capacity, and unclear governance frameworks. Equitable, transparent, and policy-aligned implementation is essential for AI to meaningfully strengthen global outbreak preparedness.
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