Artificial Intelligence for Public Health Surveillance: Promise, Inequities, and the Need for Transparent Global Adoption

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.