621 Background: Effective communication between clinical researchers and participants is vital for successful clinical trials. However, informed consent documents often contain complex language, making it difficult for many patients to fully understand key information and make informed decisions. This comprehension gap impacts patient experience and impedes trial enrollment. Large Language Models (LLMs) show promise for translating specialized medical content into accessible language; however, their effectiveness in clinical trial communication remains underexplored. Multimodal approaches combining LLM-generated content with visual aids may significantly enhance participant understanding and trust in the consent process. This study aims to design and test the feasibility of creating a lay clinical trial summary using LLM technology. Methods: This feasibility pilot leveraged an LLM to extract and customize key clinical trial information from research protocols for prospective participants. An initial literature review leveraged FDA guidance and prior work by Hill et al (2024) to establish a template and delineate content requirements. We utilized Amazon Bedrock with Anthropic's Claude 3.7 to engineer and test prompts. We extracted each information topic separately into phrases or short sentences in JavaScript Object Notation (JSON) and used placeholders to insert content into the template. Clinical experts, communication specialists, and patient advocates reviewed content for accuracy and clarity. Results: Feedback received from patient advocates were incorporated to optimize content relevance, literacy level, and acceptability before producing 2 final outputs for IRB review. Iterative prompt optimization using one-to-two shot examples in conversational, second-person language achieved optimal LLM outputs at 4-6 grade reading level. The template was converted from PowerPoint shapes to a Word table for improved text wrapping, visual presentation, and Section 508 accessibility compliance. Human oversight remained essential for content validation and managing text constraints within fixed cell dimensions. Patient reviewers provided highly positive feedback, endorsing bullet points, simplified study titles, and enhanced typography with wider spacing. Graphics and layout enhancements significantly improved engagement compared to traditional consent materials. Final outputs, after human review, were submitted to IRB for approval. Conclusions: Utilizing an LLM increased efficiency in creating summaries, provided consistency in language and structure, and customized trial content. Implementation required balancing risks of oversimplification against benefits, maintaining human oversight for critical evaluation, and ensuring regulatory compliance. A future study will evaluate participant preference for layperson abstract plus consent form versus consent form alone.
Adjei et al. (Wed,) studied this question.