AI in population health: Scaling preventive models for age-related diseases in the United States

Taiwo, Kamorudeen Abiola (2025) AI in population health: Scaling preventive models for age-related diseases in the United States. International Journal of Science and Research Archive, 16 (1). pp. 1240-1260. ISSN 2582-8185

The burden of age-related chronic diseases in the United States represents a critical public health challenge, with profound implications for healthcare sustainability and economic stability. As the nation grapples with an aging population, artificial intelligence (AI) emerges as a transformative tool for population health management, offering unprecedented capabilities for early detection, risk stratification, and preventive intervention. This comprehensive review examines the current landscape of AI applications in population health, specifically focusing on age-related diseases including cardiovascular disease, diabetes, cancer, and Alzheimer's disease. The analysis encompasses predictive modeling frameworks, implementation challenges, economic considerations, and future directions for scaling AI-driven preventive care models across diverse populations. Current evidence demonstrates that AI-powered predictive models can achieve over 80% accuracy in chronic disease risk assessment, potentially reducing healthcare costs by 10-30% through early intervention strategies. However, significant barriers persist including data quality issues, algorithmic bias, regulatory frameworks, and healthcare workforce readiness. This article provides a roadmap for healthcare systems, policymakers, and technology stakeholders to harness AI's potential while addressing implementation challenges to create sustainable, equitable population health solutions.