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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Online Access: | https://arxiv.org/abs/2508.05519 |
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| _version_ | 1866911105233715200 |
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| author | Purri, Matthew Patel, Amit Deurrell, Erik |
| author_facet | Purri, Matthew Patel, Amit Deurrell, Erik |
| contents | 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 medical data review. In a controlled experimental study with experienced medical 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. Economic analysis of a representative Phase III oncology trial reveals potential cost savings of $5.1 million, primarily driven by accelerated database lock timelines (5-day reduction saving $4.4M), improved medical review efficiency ($420K savings), and reduced query management burden ($288K savings). 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 such as database lock by 33% while maintaining regulatory compliance and significantly reducing operational costs. 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_05519 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Leveraging AI to Accelerate Medical Data Cleaning: A Comparative Study of AI-Assisted vs. Traditional Methods Purri, Matthew Patel, Amit Deurrell, Erik Computer Vision and Pattern Recognition 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 medical data review. In a controlled experimental study with experienced medical 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. Economic analysis of a representative Phase III oncology trial reveals potential cost savings of $5.1 million, primarily driven by accelerated database lock timelines (5-day reduction saving $4.4M), improved medical review efficiency ($420K savings), and reduced query management burden ($288K savings). 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 such as database lock by 33% while maintaining regulatory compliance and significantly reducing operational costs. 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. |
| title | Leveraging AI to Accelerate Medical Data Cleaning: A Comparative Study of AI-Assisted vs. Traditional Methods |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.05519 |