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.

翻译：癌症临床试验常因缺乏面向参与者的信息与教育资源而在招募和参与方面面临挑战。本研究探讨了大型语言模型（特别是GPT4）利用临床试验知情同意书生成患者友好型教育内容的潜力。基于ClinicalTrials.gov的数据，我们采用零样本学习生成试验摘要，并运用单样本学习开发多项选择题，通过患者调查和众包标注评估其有效性。结果显示，GPT4生成的摘要兼具可读性与全面性，可能提升患者对临床试验的理解与参与意愿。生成的多项选择题在众包标注中表现出较高的准确性和一致性。针对两类资源，研究均发现存在需要持续人工监督的幻觉现象。这些发现表明，大型语言模型在"开箱即用"状态下，仅需极少的试验特定工程即可支持临床试验教育材料的生成，但仍需采用人机协同机制以避免错误信息风险。