在个性化医疗信息中评估生成语言模型:仪器验证研究
Aidin Spina1, Saman Andalib1, Daniel Flores1
1School of Medicine, University of California, Irvine, Irvine, CA, United States.
JMIR AI
|August 13, 2024
概括
生成式语言模型 (GLM) 可以适应医疗文本的复杂性,以适应不同教育水平. 在定制可读性方面,GPT-4显示出比GPT-3.5更好的表现,但两者在精确的个性化方面都有局限性.
科学领域:
- 人工智能在医学中的应用
- 自然语言处理自然语言处理.
- 卫生识字研究 研究 卫生识字研究
背景情况:
- 生成式语言模型 (GLMs) 提供了改善患者对医疗信息理解的潜力.
- 可以部署GLM来应对与低健康素养相关的挑战.
研究的目的:
- 评估ChatGPT-3.5和GPT-4调整医疗文本复杂性的能力,根据患者的教育水平.
- 评估GLM作为提高健康素养的工具的实用性.
主要方法:
- 开发了II型糖尿病和高血压图片的输入模板.
- 修改了不同的假设患者教育水平的临床文本.
- 使用弗莱什阅读易度 (FKRE) 和弗莱什-金凯德等级水平 (FKGL) 评分的量化可读性.
主要成果:
- 在针对不同教育水平的文本可读性方面,GPT-4表现比GPT-3.5更好.
- 对于大多数教育群体来说,GPT-4满足了目标可读性,而GPT-3.5仅在学士水平上保持一致.
- 两种模型都显示了教育水平之间可读性调整的统计学上显著差异.
结论:
- GLM可以根据指定的教育水平修改医疗文本结构和可读性.
- 目前的GLM将可读性分为广泛的级别 (简单,中等,困难),表明精确个性化的局限性.
- 需要进一步的研究,以提高GLM在个性化医疗文本的可靠性,以提高医疗保健素养.
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