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arXiv · 2504.00934

InformGen: An AI Copilot for Accurate and Compliant Clinical Research Consent Document Generation

Zifeng Wang·Junyi Gao·Benjamin Danek·Brandon Theodorou·Ruba Shaik·Shivashankar Thati·Seunghyun Won·Jimeng Sun
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Abstract

Leveraging large language models (LLMs) to generate high-stakes documents, such as informed consent forms (ICFs), remains a significant challenge due to the extreme need for regulatory compliance and factual accuracy. Here, we present InformGen, an LLM-driven copilot for accurate and compliant ICF drafting by optimized knowledge document parsing and content generation, with humans in the loop. We further construct a benchmark dataset comprising protocols and ICFs from 900 clinical trials. Experimental results demonstrate that InformGen achieves near 100% compliance with 18 core regulatory rules derived from FDA guidelines, outperforming a vanilla GPT-4o model by up to 30%. Additionally, a user study with five annotators shows that InformGen, when integrated with manual intervention, attains over 90% factual accuracy, significantly surpassing the vanilla GPT-4o model's 57%-82%. Crucially, InformGen ensures traceability by providing inline citations to source protocols, enabling easy verification and maintaining the highest standards of factual integrity.

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Zifeng Wang, Junyi Gao, Benjamin Danek, Brandon Theodorou, Ruba Shaik, Shivashankar Thati, Seunghyun Won, Jimeng Sun. 2025-04-01. InformGen: An AI Copilot for Accurate and Compliant Clinical Research Consent Document Generation. https://arxiv.org/abs/2504.00934

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