Optimizing the key information section of informed consent documents in biomedical research: A multi-phased study using large language models

Introduction: Informed consent is a cornerstone of ethical research, but the lack of widely accepted standards for the key information (KI) section in informed consent documents (ICDs) creates challenges in institutional review board (IRB) reviews and participant comprehension. This study explored the use of GPT-4o, a large language model (collectively, AI), to generate standardized KI sections. Methods: An AI tool was developed to interpret and generate KI content from ICDs. The evaluation involved a multi-phased process where IRB subject matter experts, principal investigators (PIs), and IRB reviewers assessed the AI output for accuracy, differentiation between standard care and research, appropriate information prioritization, and structural coherence. Results: Iterative refinements improved the AI's accuracy and clarity, with initial assessments highlighting factual errors that decreased over time. Many PIs found the AI-generated sections comparable to their own and expressed a high likelihood of using the tool for future drafts. Blinded evaluations by IRB reviewers highlighted the AI tool's strengths in describing study benefits and maintaining readability. However, the findings underscore the need for further improvements, particularly in ensuring accurate risk descriptions, to enhance regulatory compliance and IRB reviewer confidence. Conclusions: The AI tool shows promise in enhancing the consistency and efficiency of KI section drafting in ICDs. However, it requires ongoing refinement and human oversight to fully comply with regulatory and institutional standards. Collaboration between AI and human experts is essential to maximize benefits while maintaining high ethical and accuracy standards in informed consent processes.