Evaluating the Effectiveness of Risk-Based Monitoring and Artificial Intelligence-Driven Strategies in Clinical Trial Management: A Data-Driven Analysis

Abstract

This study investigates risk management practices in clinical trials by analyzing a dataset containing 1,000 trial records sourced from Kaggle's name is Clinical-Risk Management Dataset. The analysis based on their operational, regulatory, ethical, and financial risks are their comparative effectiveness of monitoring frameworks like traditional oversight, Risk-Based Monitoring (RBM), and AI-driven strategies. The findings are explored the trials experienced on average three adverse events is a 24% dropout rate, and 10% cost overruns, with high-risk trials (26.4%) strongly linked toward unsuccessful outcomes. In which regulatory and operational risks were the most frequent for ethical risks had the highest share of high-risk cases (29.1%). The performed a comparative analysis revealed that Risk-Based Monitoring (RBM) achieved stronger compliance and fewer adverse events are 2.94 vs. 3.06 in traditional monitoring based on AI-driven monitoring reduced trial terminations (12.8% vs. 15.1%). The slightly lower success rates in which advanced frameworks give better stability and oversight as compared to traditional approaches. The findings stand make-sure to risk management is strongly linked to successful trial outcomes are with high-risk categories consistently associated with failure of trial phase or region. The study remains to conclude with recommendations for sponsors, regulators, and researchers to adopt data-driven frameworks and develop predictive models that are identified to underexplored domains such as ethical and financial risks.

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