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% faster, are rated as less cognitively demanding and are strongly preferred by users. While expert oversight remains essential, this suggests that AI-assisted extraction can enable protocol intelligence at scale, motivating the integration of similar methodologies into real-world clinical workflows to further validate its impact on feasibility, study start-up, and post-activation monitoring.

翻译：伴随临床试验方案复杂性增加、修订频发以及知识管理挑战，试验团队承受着沉重负担。将方案内容结构化为标准格式，有望提升效率、保障文档质量并加强合规性。我们评估了基于生成式大语言模型（LLMs）与检索增强生成（RAG）技术的人工智能系统，用于自动提取临床试验方案信息。将面向临床试验定制化RAG流程的提取准确率，与公开可用的独立LLMs进行对比，同时模拟分析AI辅助对临床研究协调员（CRC）提取工作流程的操作影响。研究发现：与基于专家支持参考标注结果相比，我们的RAG流程提取准确率（89.0%）显著高于采用优化提示词的独立LLMs（62.6%）。在模拟提取工作流中，AI辅助任务完成速度提升40%，认知负荷评级更低，且获得用户强烈偏好。尽管专家监督仍不可或缺，但该结果表明AI辅助提取可实现大规模方案智能分析，为将此类方法整合至实际临床工作流程以验证其对可行性、研究启动及上市后监测的影响提供了依据。