AI-Assisted Clinical Trial Monitoring Systems

Abstract

Clinical trials remain the cornerstone of evidence-based medicine, yet monitoring these trials for protocol deviations, safety signals, and data integrity issues has traditionally relied on resource-intensive manual site visits that cover fewer than 15% of enrolled participants. Artificial intelligence (AI) offers the potential to transform trial monitoring through continuous, data-driven surveillance that can detect anomalies across entire patient populations in real time. This study presents the AI-Assisted Clinical Trial Monitoring Framework (AI-CTMF), which evaluates five AI-based monitoring approaches -- rule-based anomaly detection, supervised classification of protocol deviations, natural language processing (NLP) for adverse event extraction, predictive risk scoring for site-level quality, and federated learning for multi-site signal detection -- across four clinical trial monitoring tasks: protocol deviation detection, adverse event identification, data quality assessment, and patient dropout prediction. Our Clinical Trial Monitoring AI Score (CTMAS) integrates deviation detection accuracy, adverse event sensitivity, data quality coverage, dropout prediction precision, and regulatory compliance alignment. Supervised classification achieved the highest CTMAS (0.912) through gradient-boosted models trained on 48,000 annotated case report forms, while NLP-based adverse event extraction achieved the highest sensitivity (0.945) for serious adverse events through transformer models fine-tuned on 12,000 clinical narratives. Federated learning demonstrated that multi-site models improve dropout prediction by 18% over single-site baselines without sharing patient-level data across institutions.