人口变量对近视教育材料可读性的影响 通过大型语言模型生成的近视教育材料
Gabriela G Lee1, Deniz Goodman1, Ta Chen Peter Chang1
1Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA.
Clinical ophthalmology (Auckland, N.Z.)
|December 9, 2024
概括
在提示符中的人口学修改器会影响双子座生成的近视信息的可读性,但不是ChatGPT或Copilot. 这突出了大语言模型 (LLM) 影响患者教育的潜在偏差.
科学领域:
- 医疗保健中的人工智能
- 生物医学信息学 生物医学信息学
- 眼科医生 眼科 眼科
背景情况:
- 大型语言模型 (LLM) 在医疗保健中越来越多地使用,造成数据偏差的风险.
- 在LLM产生的内容中的偏见可以影响提供者和患者的信息质量.
- 这项研究调查了对LLM产生的患者教育材料的人口影响.
研究的目的:
- 在近视患者教育材料的提示中描述种族,种族和性别修饰者的影响.
- 为了比较人口学变量对三个LLM的影响:ChatGPT,Gemini和Copilot.
- 根据人口变化评估LLM生成内容的可读性指标.
主要方法:
- 用于近视调查,使用了具有人口学修饰因素 (种族,族裔,性别) 的标准提示.
- 来自ChatGPT,Gemini和Copilot的响应被分析为字数,SMOG指数,Flesch-Kincaid等级水平和Flesch阅读易度.
- 采用双向ANOVA分析可读性得分的显著差异.
主要成果:
- 根据人口统计学变量,没有发现ChatGPT或Copilot的可读性有显著差异.
- 双子座在SMOG指数,Flesch-Kincaid等级水平和Flesch阅读方便性方面表现出显著的差异,这些差异取决于种族变量 (p<0.05).
- 总共分析了150个回答.
结论:
- 患者人口统计信息影响双子座生成的教育材料的可读性.
- 临床医生和患者应该意识到人口统计学对LLM生成的内容可读性的影响,例如近视.
- 需要进一步的研究来评估LLM产生的信息的准确性,并减轻错误信息的风险.
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