评估五个大型语言模型在为儿科心胸手术生成患者教育内容方面的表现:一项比较性研究
H Shafeeq Ahmed1, Diya Suhas2, Leroy D'Souza2
1Department of Medicine, Bangalore Medical College and Research Institute, K.R Road, Bangalore, Karnataka, 560002, India. shafeeqahmed2002@gmail.com.
General thoracic and cardiovascular surgery
|September 24, 2025
概括
大型语言模型 (LLM) 在儿科心胸外科手术中对患者教育有希望,但效果因程序和模型而异. 医生监督和量身定制的内容对于最佳理解至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 患者教育 患者教育
背景情况:
- 患者教育对于儿科心胸外科手术的结果至关重要.
- 大型语言模型 (LLM) 为可扩展的内容生成提供了一个潜在的解决方案.
- 评估LLM在这个专门领域的有效性是必要的.
研究的目的:
- 评估五个领先的LLM在为儿科心胸外科手术程序生成患者教育材料方面的表现.
- 根据准确性,一致性,相关性和可读性来比较LLM生成的内容.
主要方法:
- 五个LLM (ChatGPT 4o,ChatGPT 4,谷歌双子座,困惑AI,ClaudeAI) 为24个程序生成了360个小册子.
- 内容由五名审稿人评估,以使用结构化的尺度来评估准确性和一致性.
- 患者倡导者评估了相关性,六个指标衡量了可读性.
主要成果:
- 困惑AI在心脏手术中表现出色,克劳德AI在肺部手术中表现出色.
- 双子座产生了最易读的内容;困惑的人工智能显示了最高的整体相关性.
- 聊天GPT 4在一致性方面表现出很高的变化.
结论:
- 在儿科心胸外科手术患者教育方面,LLM具有显著的潜力.
- 模型性能因程序和评估指标而异.
- 定制内容,确保医生监督和优化可读性对于有效实施至关重要.
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