The use of large language models to enhance cancer clinical trial educational materials
Journal:
arXiv
Published Date:
Dec 2, 2024
Abstract
Cancer clinical trials often face challenges in recruitment and engagement
due to a lack of participant-facing informational and educational resources.
This study investigated the potential of Large Language Models (LLMs),
specifically GPT4, in generating patient-friendly educational content from
clinical trial informed consent forms. Using data from ClinicalTrials.gov, we
employed zero-shot learning for creating trial summaries and one-shot learning
for developing multiple-choice questions, evaluating their effectiveness
through patient surveys and crowdsourced annotation. Results showed that
GPT4-generated summaries were both readable and comprehensive, and may improve
patients' understanding and interest in clinical trials. The multiple-choice
questions demonstrated high accuracy and agreement with crowdsourced
annotators. For both resource types, hallucinations were identified that
require ongoing human oversight. The findings demonstrate the potential of LLMs
"out-of-the-box" to support the generation of clinical trial education
materials with minimal trial-specific engineering, but implementation with a
human-in-the-loop is still needed to avoid misinformation risks.
