Abstract: The complexity of public health clinical trials, particularly those orchestrated by federal agencies such as the NIH and CDC demand robust, adaptive, and scalable technologies to ensure timely execution and reliable outcomes. This research investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques for risk forecasting and site performance optimization in multisite clinical trials. The study focuses on predictive models designed to identify operational risks (e.g., patient dropout, protocol deviations, enrollment delays) and evaluates how these models can improve trial logistics, resource allocation, and site selection processes.
To enhance real-time monitoring and decision-making, the proposed AI framework is integrated into AWS-native environments using tools like Apache Airflow for pipeline orchestration, EC2 for scalable compute resources, and AWS Lambda for event-driven processing. Through simulation and case study analysis, we demonstrate how the system facilitates adaptive responses to public health emergencies such as pandemics, vaccine trials, or regional disease outbreaks.
Furthermore, the study explores practical deployments within the NIH and CDC clinical research ecosystem, illustrating how AI-driven dashboards can aid in forecasting operational bottlenecks, automating compliance reporting, and enhancing site-level performance visibility. The outcomes suggest that AI-integrated platforms not only increase efficiency but also significantly reduce trial risks and costs. The findings support a paradigm shift in how large-scale public health trials are managed, offering a blueprint for future-ready, AI-powered clinical research infrastructure.
Keywords: Public Health Clinical Trials, AI-Enabled Risk Forecasting, Site Perfomance Optimization, Machine Learning in Clinical Research, AWS for Clinical Trials, Apache Airflow, EC2 and AWS Lambda, NIH Emergency Response, CDC Trials, Predictive Analytics, Trial Logistics Optimization, Real-Time Site Monitoring, Federated Trial Intelligence, Adaptive Trial Management
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DOI:
10.17148/IJARCCE.2025.14701
