What question did this study set out to answer?

The aim is to assess how AI can enhance public health surveillance while addressing inequities in implementation.

April 23, 2026

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

Key Points

The aim is to assess how AI can enhance public health surveillance while addressing inequities in implementation.
Analysis of current AI surveillance systems in high-income countries
Identification of barriers to adoption in low- and middle-income countries
Evaluation of the need for transparent governance frameworks
AI systems show high potential to improve outbreak preparedness.
Implementation is limited in low-income regions due to existing data and infrastructure inequities.
Transparent and policy-aligned strategies are needed for equitable AI adoption.

Abstract

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.

Demander à l'IA

Bookmark