[2602.00052] AI-assisted Protocol Information Extraction For Improved Accuracy and Efficiency in Clinical Trial Workflows

Computer Science > Information Retrieval arXiv:2602.00052 (cs) [Submitted on 19 Jan 2026 (v1), last revised 16 Apr 2026 (this version, v2)] Title:AI-assisted Protocol Information Extraction For Improved Accuracy and Efficiency in Clinical Trial Workflows Authors:Ramtin Babaeipour, François Charest, Madison Wright View a PDF of the paper titled AI-assisted Protocol Information Extraction For Improved Accuracy and Efficiency in Clinical Trial Workflows, by Ramtin Babaeipour and 2 other authors View PDF HTML (experimental) Abstract:Increasing clinical trial protocol complexity, amendments, and challenges around knowledge management create significant burden for trial teams. Structuring protocol content into standard formats has the potential to improve efficiency, support documentation quality, and strengthen compliance. We evaluate an Artificial Intelligence (AI) system using generative LLMs with Retrieval-Augmented Generation (RAG) for automated clinical trial protocol information extraction. We compare the extraction accuracy of our clinical-trial-specific RAG process against that of publicly available (standalone) LLMs. We also assess the operational impact of AI-assistance on simulated extraction Clinical Research Coordinator (CRC) workflows. Our RAG process shows higher extraction accuracy (89.0%) than standalone LLMs with fine-tuned prompts (62.6%) against expert-supported reference annotations. In simulated extraction workflows, AI-assisted tasks are completed 40% fas...