将大型语言模型作为健康素养工具进行比较:评估和简化关于性别确认手术的文本
Victoria N Yi1, Angel P Scialdone1, Ann Marie Flusche1
1Duke University School of Medicine, Durham, North Carolina, USA.
Journal of health communication
|August 19, 2025
概括
对于性别确认手术的在线患者资料太复杂了. 大型语言模型 (LLM) 可以简化这些文本,Google Gemini显示了改善患者理解的最大希望.
科学领域:
- 医疗信息学 医疗信息学
- 患者教育 患者教育
- 自然语言处理自然语言处理.
背景情况:
- 针对性别确认手术的面向患者的材料往往超过了推的八年级阅读水平.
- 可访问的健康信息对于患者的知情决策至关重要.
研究的目的:
- 评估和比较各种大型语言模型 (LLM) 的可读性分析和文本简化能力,用于在线患者资源的性别确认程序.
- 评估LLM在将这些材料的阅读水平降低到推的八年级水平方面的有效性.
主要方法:
- 在Google Incognito中搜索了15个确认性别的外科术语.
- 使用可读性评分系统v2.0 (RSS) 分析了前20个文本结果的可读性.
- 利用八个LLM来评估和简化文本到八年级的阅读水平,随后由RSS进行重新评估.
主要成果:
- 在线资源的平均阅读成绩为12.66.
- 谷歌Gemini获得了最低的简化阅读水平 (8.39),其次是Anthropic Claude (9.53) 和ChatGPT 4 (10.19).
- 在女性化/面部手术和简化生殖器手术方面,LLM的错误率增加.
结论:
- 对于性别确认程序的在线患者材料比推的要复杂得多.
- 虽然LLM显示出提高可读性的潜力,但其性能各不相同,需要谨慎的临床应用.
- 需要进一步的研究来解决LLM在性别肯定护理患者教育中的变化和偏见.
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