使用大型语言模型生成有关膜炎的教育材料
Reza Kianian1, Deyu Sun1, Eric L Crowell2
1Stein Eye Institute, Department of Ophthalmology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.
Ophthalmology. Retina
|September 16, 2023
概括
像ChatGPT这样的大型语言模型可以简化膜炎的复杂医学信息,使患者更容易获得. 这项技术在改善在线健康内容的可读性方面显示出前景.
科学领域:
- 人工智能在医学中的应用
- 医疗信息学 医疗信息学
- 医学沟通 医学沟通
背景情况:
- 在线健康信息经常使用复杂的语言,阻碍患者的理解.
- 尿道炎信息需要清晰,可访问的沟通,以有效地参与患者.
研究的目的:
- 评估大型语言模型 (LLM) 产生可读的针对患者的膜炎信息的能力.
- 评估LLMs在提高现有在线紫外线炎健康内容可读性方面的有效性.
主要方法:
- 聊天GPT和巴德被要求在6年级的阅读水平上使用弗莱什-金凯德等级水平 (FKGL) 公式生成卵膜炎信息.
- 此外,LLM还被要求重写现有的在线紫外线炎内容以提高可读性.
- 用FKGL和其他指标评估生成和重写内容的可读性.
主要成果:
- 与Bard (平均FKGL 10.5) 相比,ChatGPT产生了明显更容易理解的卵巢炎信息 (FKGL 6.3的平均值).
- 聊天GPT成功地降低了现有的在线脑膜炎内容的可读性,从平均成绩水平的11.0降至8.0.
- 巴德无法显著提高现有的在线脑膜炎内容的可读性.
结论:
- 聊天GPT在帮助临床医生为患者创建更易于理解的脑膜炎健康信息方面表现出潜力.
- 在针对患者的脑膜炎教育材料中,LLM可以成为减少语言复杂性的有价值工具.
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