使用大型语言模型来增强癌症临床试验教育材料
Mingye Gao1, Aman Varshney2, Shan Chen3,4
1Massachusetts Institute of Technology, Cambridge, MA 02139, United States.
JNCI cancer spectrum
|February 8, 2025
概括
大型语言模型 (LLM) 可以为癌症临床试验创建患者友好的教育材料,提高理解和参与度. 人类监督对于确保准确性至关重要,特别是当信息在试验文件中有限时.
科学领域:
- 人工智能在医学中的应用
- 临床试验 患者教育
- 自然语言处理自然语言处理.
背景情况:
- 患者的认识和理解对于癌症临床试验的成功至关重要.
- 大型语言模型 (LLM) 显示出患者教育的潜力,但尚未被用于临床试验.
- 本研究评估了LLM在为试验参与者生成教育内容方面的表现和风险.
研究的目的:
- 评估LLM在为癌症临床试验创建患者友好的教育内容方面的有效性.
- 探索与LLM生成的临床试验信息相关的风险.
- 评估LLM提高患者对临床试验的理解和兴趣的能力.
主要方法:
- 生成预训练变压器4 (GPT4) 用于从临床试验同意表格中生成摘要和问答对.
- 零射击和一射击学习方法用于总结和问答生成.
- 患者调查和众包注释评估了生成内容的准确性和有效性.
主要成果:
- 这两种LLM提示方法都产生了可比,可理解的摘要,改善了患者的理解和兴趣.
- 产生的多选题显示出高准确度和与人类注释者一致的结果.
- 不准确的信息是最常见的,当LLMs被提示在源同意表格中不清楚存在的细节时.
结论:
- 像GPT4这样的LLM在产生患者友好的临床试验教育材料方面表现有前途,专业提示最小.
- 这些发现为LLM在加强患者教育和参与临床试验方面提供了概念证明.
- 持续的人类监督对于确保LLM生成的临床试验内容的准确性至关重要.
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