评估大型语言模型在根据阅读水平生成皮肤病患者教育材料中的应用:定性研究
Raphaella Lambert1, Zi-Yi Choo1, Kelsey Gradwohl2
1Pritzker School of Medicine, University of Chicago, Chicago, IL, United States.
大型语言模型 (LLM) 可以在较低的阅读水平上生成皮肤病患者教育材料 (PEM). 在五年级水平上,GPT-4表现出色,而在七年级水平上,ChatGPT-3.5和DocsGPT在罕见疾病方面表现更好.
科学领域:
- 医疗信息学 医疗信息学
- 皮肤病学 皮肤病学
- 卫生识字 卫生识字
背景情况:
- 皮肤病患者教育材料 (PEMs) 通常超过全国平均阅读水平.
- 像ChatGPT-3.5,GPT-4,DermGPT和DocsGPT这样的大型语言模型 (LLM) 显示了内容生成的潜力.
研究的目的:
- 评估LLM在指定的读数水平上产生皮肤PEM的能力.
- 评估常见和罕见皮肤病的LLM产生的PEM中意义的保存.
主要方法:
- 现有PEM的Flesch-Kincaid读数水平 (FKRL) 与LLM生成的内容进行比较.
- 促使四个LLM在第五级和第七级FKRL创建PEM用于常见和罕见的皮肤病.
- 通过皮肤病学居民实习生的评估来评估意义的保存.
主要成果:
- 现有的PEM的平均水平是九年级的FKRL.
- 由LLM生成的PEM满足了五年级和七年级的FKRL,GPT-4在五年级表现良好.
- 对于七年级水平,ChatGPT-3.5和DocsGPT在罕见疾病中表现优于GPT-4,DermGPT在常见疾病中表现高准确.
结论:
- LLM可以生成可访问的皮肤学PEM,满足目标读数水平.
- GPT-4,ChatGPT-3.5,DocsGPT和DermGPT在改善皮肤病学中的健康素养方面表现有前途.
- 通过LLM生成的PEM在很大程度上是准确和可理解的,有助于患者的理解.
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