使用大型语言模型生成关于童年玻璃眼的教育材料.
Qais Dihan1, Muhammad Z Chauhan2, Taher K Eleiwa3
1Chicago Medical School (Q.D.), Rosalind Franklin University of Medicine and Science, North Chicago, Illinois, USA; Department of Ophthalmology (Q.D., M.Z.C., A.B.S., A.M.E.), Harvey and Bernice Jones Eye Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA.
American journal of ophthalmology
|April 13, 2024
概括
大型语言模型 (LLM) 产生了高质量的患者教育材料,用于儿童眼. 聊天GPT-4展示了卓越的可读性和准确性,有效地改善了在线信息的可访问性.
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) 对于治疗儿童绿眼病至关重要.
- 现有的在线信息可能具有低于最佳的可读性,妨碍患者的理解.
- 大型语言模型 (LLM) 提供了产生和改进医疗内容的潜力.
研究的目的:
- 评估LLM生成的儿童绿眼病PEM的质量,可读性和准确性.
- 评估LLM在提高现有在线资源可读性的有效性.
- 为了比较不同LLM (ChatGPT-3.5,ChatGPT-4,Bard) 在创建可访问的患者信息方面的表现.
主要方法:
- 一项横截面的比较研究评估了LLM生成的PEM,使用特定的提示提示来理解和分年级级的阅读.
- 通过DISCERN问卷和患者教育材料评估工具 (PEMAT) 评估质量.
- 可读性是使用简单测量Gobbledygook (SMOG) 和Flesch-Kincaid等级水平 (FKGL) 公式来测量的. 使用利克特错误信息量表来评估准确度. 此外,LLMs还重写了现有的在线资源.
主要成果:
- 所有经过测试的LLM都产生了高质量,易于理解和准确的PEM,用于儿童青光瘤.
- 与一般提示 (提示A) 相比,使用特定年级级提示 (提示B) 生成的PEM显示出明显改善的可读性.
- 聊天GPT-4始终产生了最易读的内容,并且是唯一一个在重写现有材料时达到目标6年级阅读水平的模型.
结论:
- 作为创建高质量,准确的儿童玻璃眼患者教育材料的工具,LLM显示出显著的希望.
- 这些模型可以有效地提高现有的在线健康信息的可读性.
- 聊天GPT-4在生成可访问和科学合理的患者教育内容方面表现出特别强大的优势.
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