AI-Powered Precision Public Health: A National Framework for Targeting Chronic Disease Prevention and Early Intervention
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V7I3P102Keywords:
Artificial Intelligence, Precision Public Health, Chronic Disease Prevention, Predictive Analytics, Early Intervention, Health Equity, National Health PolicyAbstract
Chronic diseases remain among the leading causes of preventable illness, disability, mortality, and healthcare expenditure in many national health systems. Traditional public health approaches have contributed significantly to disease prevention, but they often rely on broad population-level strategies, delayed surveillance, and reactive intervention models that may fail to identify high-risk individuals and communities early enough. This paper proposes an AI-powered precision public health framework for targeting chronic disease prevention and early intervention at national level. The framework integrates electronic health records, public health surveillance data, social determinants of health, genomic risk information, and predictive analytics to support earlier risk detection, population segmentation, and tailored prevention strategies. It emphasizes the use of artificial intelligence not as a replacement for public health expertise, but as a decision-support mechanism for improving resource allocation, screening, care coordination, and intervention timing. The paper also highlights the ethical, governance, and equity challenges associated with AI implementation, including algorithmic bias, privacy risks, data quality limitations, transparency, and public trust. It argues that AI-powered precision public health can strengthen chronic disease prevention when implemented through responsible governance, human oversight, equity-focused design, and continuous learning health systems. The proposed framework provides a national policy direction for improving early detection, reducing preventable complications, and promoting more targeted and equitable health outcomes.
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