PREDICTIVE ARTIFICIAL INTELLIGENCE MODELS FOR EARLY DETECTION AND MANAGEMENT OF CHRONIC DISEASES TO STRENGTHEN NATIONAL HEALTHCARE RESILIENCE IN THE UNITED STATES

Abstract

Chronic diseases remain the leading drivers of morbidity, mortality, and healthcare expenditure in the United States, underscoring a national need for earlier detection and more proactive management strategies. Advances in predictive artificial intelligence (AI) have created new opportunities for identifying risk trajectories, monitoring disease progression, and supporting clinical decision-making across large and heterogeneous patient populations. This study investigates the performance, methodological structure, and clinical relevance of predictive AI models applied to longitudinal datasets for early detection and chronic disease management within U.S. healthcare systems. Using electronic health records, imaging outputs, laboratory sequences, wearable data, and temporal clinical indicators, the analysis evaluates model discrimination, calibration, feature representation, and subgroup performance across major chronic conditions including cardiovascular disease, diabetes, chronic kidney disease, respiratory disorders, and oncological pathways. Quantitative case study methods, supported by multivariate modeling, temporal analyses, and diagnostic performance metrics, reveal that predictive AI systems can identify high-risk individuals with strong accuracy (AUC 0.80–0.88), consistent calibration, and meaningful alignment with biomarker trajectories. Findings also show that predictive performance varies across demographic segments and comorbidity burdens, reflecting structural differences in physiological patterns and healthcare utilization. Data reliability assessments confirm that model behavior remains stable under conditions of missingness, irregular sampling, and multimodal integration. Overall, the study demonstrates that predictive AI models provide a statistically coherent and clinically relevant approach to supporting early detection and management of chronic diseases. These results highlight the capacity of predictive analytics to strengthen national healthcare resilience by improving risk stratification, enhancing longitudinal monitoring, and enabling earlier intervention across chronic disease populations.