Large Language Model-Assisted Review of Clinical Trial Informed Consent for Data Reuse: Retrieval-Augmented Generation Approach.

Background: Regulatory frameworks such as the Belmont Report, the Common Rule, and the Declaration of Helsinki require informed consent to ensure participants understand a study's purpose and can make voluntary decisions about their involvement. Regulations, including the General Data Protection Regulation (Regulation [EU] 2016/679), further emphasize that consent must be freely given and revocable without disadvantage. Although informed consent forms (ICFs) are intended to be clear and accessible, they have become increasingly lengthy and complex. Large language models (LLMs) offer the potential to navigate and interpret this complexity and have shown promise in biomedical information extraction tasks. However, their susceptibility to hallucinations limits their reliability in high-stakes settings. Retrieval-augmented generation (RAG) can mitigate such errors. Objective: This study evaluates the integration of GPT-4.1 with RAG for reviewing data reuse language in ICFs and its ability to interpret complex ICF language. Methods: First, we processed 438 ICFs from different trials, including multiple countries, languages, and versions of ICFs. Using expertly curated prompts, we extracted information about data reuse using GPT-4.1. By comparing the LLM-generated data reuse outputs with human expert ground truth, we evaluated accuracy and the time required to extract information for each ICF. To further validate the workflow, we evaluated an independent set of 488 ICFs spanning additional trials, languages, and regions. During the validation phase, the workflow was evaluated using a baseline assessment (89 ICFs) followed by 3 iterative optimization rounds assessed on 14, 281, and 104 ICFs, respectively. For this cohort, we assessed the correctness of LLM outputs along with the quality of supporting evidence provided by the model. Results: Across the initial set of 438 ICFs, the system achieved 81.6% accuracy. During validation, the baseline accuracy using the preoptimized prompts was 84.0% (89 documents). Accuracy improved sequentially across the 3 iterative optimization rounds, increasing from 82.3% in the first round (14 documents) to 90.7% in the second round (281 documents), and reaching 92.1% in the third and final round on an independent holdout cohort of 104 documents. Ultimately, using our RAG-based approach, the system accurately extracted data reuse information across multiple languages. Conclusions: Our RAG-based workflow demonstrated the ability to effectively support the ICF review process for data reuse and interpret complex consent language, contributing to improved consistency in ICF evaluations for data reuse.