Scholarlink

<p>Informed consent forms are essential for protecting patient rights, but are often difficult to understand, especially in non-English settings where formal language and long, context-dependent sentences hinder comprehension. This study evaluated whether large language models (LLMs) can simplify Chinese language surgical consent forms and whether clinician revision can further improve the output. Official forms from nine hospitals were used to create three versions of each document: the original, an LLM-simplified version, and a clinician-revised version. We assessed text structure, readability, content quality, and layperson comprehension using quantitative metrics and expert ratings. The LLM version improved readability and comprehension but reduced content quality, particularly risk information. Clinician revision restored accuracy while maintaining clarity and achieved the highest comprehension scores. Linear mixed effects modeling confirmed these trends. These findings highlight both the impact of LLM-based simplification and the value of human AI collaboration in patient communication.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Evaluating large language models for simplifying non-English medical consent with clinician involvement

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月1日

查看全文
  • Jianchen Luo,
  • Jing Ma,
  • Yiwen Qiu,
  • Tao Wang,
  • Yi Yang,
  • Guoteng Qiu,
  • Hao Chen,
  • Jiayuecheng Pang,
  • Wentao Wang

摘要

Informed consent forms are essential for protecting patient rights, but are often difficult to understand, especially in non-English settings where formal language and long, context-dependent sentences hinder comprehension. This study evaluated whether large language models (LLMs) can simplify Chinese language surgical consent forms and whether clinician revision can further improve the output. Official forms from nine hospitals were used to create three versions of each document: the original, an LLM-simplified version, and a clinician-revised version. We assessed text structure, readability, content quality, and layperson comprehension using quantitative metrics and expert ratings. The LLM version improved readability and comprehension but reduced content quality, particularly risk information. Clinician revision restored accuracy while maintaining clarity and achieved the highest comprehension scores. Linear mixed effects modeling confirmed these trends. These findings highlight both the impact of LLM-based simplification and the value of human AI collaboration in patient communication.