大型语言模型:儿童白内障患者教育的新前沿
Qais Dihan1,2, Muhammad Z Chauhan2, Taher K Eleiwa3
1Rosalind Franklin University of Medicine and Science Chicago Medical School, North Chicago, Illinois, USA.
The British journal of ophthalmology
|August 22, 2024
概括
大型语言模型 (LLM) 有效地生成和改进了关于儿科白内障的患者教育材料. 聊天GPT-4在创建可读和准确的内容以改善患者理解方面表现出卓越的表现.
科学领域:
- 眼科医生 眼科 眼科
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 患者教育材料 (PEMs) 对于治疗儿科白内障至关重要.
- 评估大型语言模型 (LLM) 在创建和完善这些材料中的实用性至关重要.
研究的目的:
- 评估三个LLM (ChatGPT-3.5,ChatGPT-4,谷歌巴德) 在产生新的和改进儿童白内障现有的PEM的能力.
- 评估LLM生成的PEM的质量,可理解性,可操作性,准确性和可读性.
主要方法:
- 一项使用三个提示来评估LLM绩效的横截面比较研究.
- 评估指标包括DISCERN质量评分,患者教育材料评估工具 (PEMAT) 的可理解性和可操作性,Likert尺度的准确性和SMOG/Flesch-Kincaid等级水平 (FKGL) 的可读性.
- 来自ChatGPT-3.5,ChatGPT-4和谷歌巴德的回复的比较.
主要成果:
- 所有LLM生成的PEM都是高质量的,易于理解的,准确的,尽管不总是可操作的.
- LLM,特别是ChatGPT-4,显著提高了儿科白内障材料的可读性,达到六年级目标阅读水平.
- 与其他模型相比,ChatGPT-4产生了更容易阅读的内容,SMOG和FKGL分数较低.
结论:
- LLM,特别是ChatGPT-4,是开发高质量,可读性和准确的儿科白内障患者教育材料的宝贵工具.
- 法律医疗法可以有效地提高现有的患者教育材料的可读性,改善患者和家人的可访问性.
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