The use of large language models to enhance cancer clinical trial educational materials.

ABSTRACT:

Background

Adequate patient awareness and understanding of cancer clinical trials is essential for trial recruitment, informed decision making, and protocol adherence. Although large language models (LLMs) have shown promise for patient education, their role in enhancing patient awareness of clinical trials remains unexplored. This study explored the performance and risks of LLMs in generating trial-specific educational content for potential participants.

Methods

Generative Pretrained Transformer 4 (GPT4) was prompted to generate short clinical trial summaries and multiple-choice question-answer pairs from informed consent forms from ClinicalTrials.gov. Zero-shot learning was used for summaries, using a direct summarization, sequential extraction, and summarization approach.

SUBMITTER: Gao M 

PROVIDER: S-EPMC12362247 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature