Digital Equity in Global Health AI: Why Low-Income Countries Are Left Behind by Pandemic Intelligence Systems

Abstract

Artificial intelligence systems for pandemic surveillance and outbreak detection have demonstrated substantial technical capability in high-income countries, yet they remain largely absent from the public health systems of low and middle-income countries, precisely the settings that bear the highest infectious disease burden. This paper examines the structural reasons for this digital equity gap, drawing on global health literature, health informatics research, and case evidence from deployed AI surveillance systems. We identify six structural deficit dimensions spanning health data infrastructure, compute capacity, human capital, research funding, regulatory environment, and algorithmic representativeness. We present a comparative analysis of major pandemic intelligence systems against equity benchmarks, and propose a set of technical, institutional, and governance interventions grounded in published evidence. The analysis demonstrates that the AI equity gap is self-reinforcing: the absence of LMIC data in training sets produces models that perform poorly in LMIC settings, reducing adoption and perpetuating the absence. Breaking this cycle requires deliberate design choices, targeted funding, and WHO-led governance reform.