Integrating Artificial Intelligence and Public Health Systems for Early Disease Detection: A Multidisciplinary Approach
DOI:
https://doi.org/10.71465/hjmri.382Keywords:
Artificial Intelligence, Public Health, Disease Surveillance, Early Detection, Machine Learning, Health InformaticsAbstract
The rapid advancement of Artificial Intelligence (AI) has created new opportunities for transforming public health systems, particularly in the early detection of diseases. This study explores the integration of AI technologies with public health infrastructure to enhance disease surveillance, diagnosis, and response mechanisms. By combining expertise from computer science, epidemiology, medicine, and policy studies, this multidisciplinary approach enables more accurate and timely identification of health threats. The paper analyzes existing AI-based diagnostic models, discusses data-driven surveillance systems, and evaluates ethical and operational challenges. The findings indicate that AI-powered tools significantly improve predictive accuracy and decision-making processes. However, successful implementation requires robust data governance, skilled human resources, and inter-sectoral collaboration. The study highlights future directions for developing intelligent, resilient, and equitable public health systems
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Copyright (c) 2026 Michael Anderson (Author)
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