AI-assisted Protocol Information Extraction For Improved Accuracy and Efficiency in Clinical Trial Workflows

2018-08-07 · Source: cs.AI updates on arXiv.org · Field: Health & Wellbeing — Health & Medical Research, Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

A Banting Health AI study evaluated an AI system using generative LLMs with Retrieval-Augmented Generation (RAG) for automated clinical trial protocol information extraction. The RAG process achieved 87.8% accuracy, significantly outperforming standalone LLMs with fine-tuned prompts (62.6%) against expert-supported reference annotations. In simulated workflows, AI-assisted tasks were completed at least 40% faster, were rated as less cognitively demanding, and were strongly preferred by users. The system employs a clinical-trial-specific RAG process, including a specialized two-stage approach for Schedule of Events (SoE) extraction using table detection and vision-based multimodal generation. This methodology aims to improve efficiency, documentation quality, and compliance in clinical trial workflows by structuring complex protocol content.

Key takeaway

For NLP Engineers developing solutions for clinical research, integrating a specialized RAG system with multimodal capabilities for protocol information extraction can drastically improve data accuracy and operational efficiency. You should prioritize RAG for tasks involving complex, lengthy documents and tabular data like the Schedule of Events, as it reduces manual effort and enhances compliance. Consider pilot deployments to validate its impact on study start-up and post-activation monitoring, ensuring robust performance and safety monitoring.

Key insights

AI-assisted RAG significantly boosts clinical trial protocol data extraction accuracy and efficiency over standalone LLMs.

Principles

• RAG mitigates context confusion in lengthy documents.
• Hybrid human-AI annotation improves ground truth scalability.
• Multimodal LLMs excel at complex tabular data extraction.

Method

The RAG process involves document chunking, custom retrieval queries, and structured information generation using a generation LLM. SoE extraction uses transformer-based table detection followed by multimodal LLM vision-based extraction.

In practice

• Use RAG for complex, scattered information extraction.
• Implement context-aware chunking for hierarchical documents.
• Employ LLM-as-a-judge for scalable content evaluation.

Topics

• Clinical Trial Protocols
• Information Extraction
• Retrieval-Augmented Generation
• Large Language Models
• Schedule of Events

Best for: NLP Engineer, AI Scientist, Research Scientist, Domain Expert

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.