InformGen: An AI Copilot for Accurate and Compliant Clinical Research Consent Document Generation
This addresses the problem of automating high-stakes document generation for clinical researchers and regulators, though it is incremental as it builds on existing LLMs with optimizations.
The paper tackles the challenge of generating accurate and compliant informed consent forms (ICFs) for clinical research using large language models (LLMs), achieving near 100% compliance with regulatory rules and over 90% factual accuracy with human intervention.
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.