用大模型自动生成癌症临床试验科普内容，提升患者理解与参与意愿。
The use of large language models to enhance cancer clinical trial educational materials
- 直接用GPT4零样本生成试验摘要，少量样本生成测验题。
- 生成内容可读性强、准确度高，患者理解度和兴趣显著提升。
- 适合医疗教育、临床研究者使用，需人工审核防幻觉。
癌症临床试验常因缺乏面向患者的教育资料而面临招募难、参与度低的问题。本研究探索了大型语言模型（特别是GPT4）从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数据，采用零样本学习生成试验摘要，一元样本学习设计多项选择题，通过患者问卷与众包标注评估效果。结果显示，GPT4生成的摘要兼具可读性与全面性，可能提升患者对试验的理解与兴趣；生成的测验题在准确性与标注者一致性上表现良好。但两类内容均发现幻觉现象，需持续人工监督。研究证明，大模型无需针对特定试验进行工程改造即可“即插即用”生成教育材料，但必须保留人机协同机制以规避错误信息风险。
原文摘要 · Abstract (English)
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.
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