Leveraging AI to Accelerate Clinical Data Cleaning: A Comparative Study of AI-Assisted vs. Traditional Methods

Clinical trial data cleaning represents a critical bottleneck in drug development, with manual review processes struggling to manage exponentially increasing data volumes and complexity. This paper presents Octozi, an artificial intelligence-assisted platform that combines large language models with domain-specific heuristics to transform clinical data review. In a controlled experimental study with experienced clinical reviewers (n=10), we demonstrate that AI assistance increased data cleaning throughput by 6.03-fold while simultaneously decreasing cleaning errors from 54.67% to 8.48% (a 6.44-fold improvement). Crucially, the system reduced false positive queries by 15.48-fold, minimizing unnecessary site burden. These improvements were consistent across reviewers regardless of experience level, suggesting broad applicability. Our findings indicate that AI-assisted approaches can address fundamental inefficiencies in clinical trial operations, potentially accelerating drug development timelines and reducing costs while maintaining regulatory compliance. This work establishes a framework for integrating AI into safety-critical clinical workflows and demonstrates the transformative potential of human-AI collaboration in pharmaceutical clinical trials.