人工智能生成的患者教育材料用于Helicobacter pylori感染:比较分析
Shuyan Zeng1,2, Qingzhou Kong1,2, Xiaoqi Wu1,2
1Department of Gastroenterology, Qilu Hospital of Shandong University, Jinan, Shandong, China.
Helicobacter
|August 4, 2024
概括
大型语言模型 (LLM) 显示出创建有关Helicobacter pylori的患者教育材料的希望. 虽然准确性和可理解性都很好,但为了有效的患者教育,完整性需要改进.
科学领域:
- 医学教育 医学教育
- 医疗保健中的人工智能
- 卫生沟通健康沟通
背景情况:
- 患者教育对于公众对Helicobacter pylori (H. pylori) 的认识至关重要.
- 大型语言模型 (LLM) 为提高患者教育提供了转变的机会.
- 这项研究评估了LLM生成的H. pylori患者教育材料 (PEM) 与医生创建的内容之间的质量.
研究的目的:
- 评估LLM生成的患者教育材料 (PEMs) 对H. pylori的质量.
- 在准确性,完整性和可理解性方面,将LLM生成的PEM与医生来源的PEM进行比较.
- 在六年级的阅读水平上,以英语和中文两种语言评估PEM.
主要方法:
- 一名医生和五名法学士 (Bing Copilot,Claude 3 Opus,Gemini Pro,ChatGPT-4,ERNIE Bot 4.0) 产生了H. pylori的PEMs. 这是一个很好的例子.
- 胃肠科医生和患者对PEM的准确性,完整性和可理解性进行了评估.
- 使用Flesch-Kincaid和Gobbledygook的简单测量得分来评估可读性.
主要成果:
- 来自所有来源的英语PEM显示了可接受的准确性和可理解性,但缺乏完整性.
- 医生来源的PEM获得了最高的准确性 (5.60),而LLM英语PEM在4.00至5.40之间.
- 与英语版本相比,中国LLM生成的PEM在准确性和完整性方面通常得分较低;没有PEM达到目标阅读水平.
结论:
- 临床医学课程展示了作为患者教育工具的潜力.
- 由LLM生成的PEM的准确性和可理解性足够,但完整性需要增强.
- 进一步优化是必要的,以解决语言多样性,并提高LLM产生的教育材料的整体可行性.
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