Innovations in Integrating Artificial Intelligence for Automated Surveillance and Outbreak Detection in Public Health Intelligence

The integration of Artificial Intelligence (AI) in public health surveillance is transforming the identification and management of outbreaks. Traditional systems typically suffer from delay, isolated data, and reactive designs, which hamper their potential for timely detection of emerging threats. Recent developments in machine learning, natural language processing, and real-time data fusion are shifting the paradigm to proactive and programmatic surveillance. This article analyzes the technical basis, current application, and strategic implications of AI-based surveillance for outbreak identification. A modular approach is proposed to guide implementation across contexts. Drawing on international case studies, from COVID-19 early detection onwards, the article lays out the promise and risks of embedding AI in modern public health systems.