THE ROLE OF ARTIFICIAL INTELLIGENCE IN PUBLIC HEALTH SURVEILLANCE: A POST-PANDEMIC PERSPECTIVE

Abstract

Background: The integration of artificial intelligence (AI) into public health surveillance has gained significant momentum, particularly following the COVID-19 pandemic. With global health systems facing unprecedented challenges, AI has demonstrated utility in outbreak detection, disease forecasting, and healthcare resource optimization. Its relevance continues to grow as public health authorities seek tools that can provide timely, accurate, and scalable responses to current and future health threats. Objective: This narrative review aims to explore the evolving role of AI in public health surveillance from a post-pandemic perspective, highlighting its applications, benefits, limitations, and future potential in clinical and policy contexts. Main Discussion Points: The review synthesizes recent literature across eight high-quality studies, focusing on thematic areas such as AI-enabled outbreak detection, predictive modelling, healthcare system optimization, and global surveillance integration. It critically examines the methodological strengths and weaknesses of current studies, including biases, variability in outcome measures, and issues related to generalizability and ethical implementation. Limitations such as selection bias, inconsistent validation practices, and lack of real-world implementation data are discussed in depth. Conclusion: AI holds transformative promise in enhancing public health surveillance, yet its integration must be guided by rigorous, inclusive, and context-sensitive research. Policymakers and clinicians are encouraged to adopt AI cautiously while further studies—especially those involving diverse populations and robust study designs—are needed to ensure ethical and effective deployment.

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