International Journal of Science, Engineering and TechnologyAuthors: Sunil Kumar
Abstract: The past decade has witnessed a remarkable transformation in public health surveillance, notably due to the integration of artificial intelligence (AI) technologies. As the world faces complex health challenges such as emerging infectious diseases, environmental threats, and the growing burden of non-communicable diseases, traditional surveillance methods have often proved inadequate for timely detection and response. AI leverages sophisticated computational power, pattern recognition, and predictive analytics to process vast, heterogeneous health data rapidly and with greater accuracy. These technologies enable real-time data acquisition and integration, facilitating earlier identification of outbreaks, monitoring of disease spread, and evaluation of intervention strategies. Applications of AI in public health range from syndromic surveillance and automated reporting systems to machine learning-based prediction models for epidemic forecasting and risk analysis. However, alongside these advancements, important challenges must be addressed including data privacy, algorithmic bias, and the need for cross-sectoral collaboration. This article explores the evolution, current applications, and future prospects of AI in public health surveillance, examining the transformative power and limits of these technologies. Harnessing AI’s full potential in public health surveillance requires a nuanced approach that integrates technological innovation with ethical guidelines, policy adaptation, and a strengthened public health workforce. As AI continues to reshape the landscape of disease detection and prevention, it holds promise not only for rapid response but also for proactive management of health threats at scale. This synthesis aims to provide a comprehensive overview of AI’s growing role in public health surveillance, reviewing major concepts, critical technologies, case studies, governance issues, ethical concerns, and emerging trends, and concluding with recommendations for harnessing AI effectively in future public health strategies.
DOI: https://doi.org/10.5281/zenodo.16978315
