利用大型语言模型来增强鼻科患者教育资源
Ariana L Shaari1, Rebecca A Ho1, Annie Xu1
1Department of Otolaryngology-Head & Neck Surgery, Rutgers New Jersey Medical School, Newark, New Jersey, USA.
The Annals of otology, rhinology, and laryngology
|May 29, 2025
概括
大型语言模型 (LLM) 显著提高了鼻病患者教育材料 (PEMs) 的可读性. 在经过测试的AI平台中,Google Gemini表现出了最高的可读性,使医疗信息更容易获得.
科学领域:
- 医学教育 医学教育
- 医疗保健中的人工智能
- 提高健康素养 提高健康素养
背景情况:
- 评估了美国鼻科学会 (ARS) 关于鼻科疾病和手术的患者教育材料 (PEM).
- 评估现有医疗信息的可读性对于患者的理解至关重要.
研究的目的:
- 将ARS生成的PEM的可读性与大型语言模型 (LLM) 创建的PEM进行比较.
- 确定LLM是否可以将复杂的医疗信息简化到六年级的阅读水平.
主要方法:
- 通过使用Flesch Kincaid等级水平 (FKGL) 和Flesch Kincaid阅读方便 (FKRE) 来分析41个ARS PEM的可读性.
- 三个LLM (ChatGPT 4.0,谷歌双子,微软Copilot) 将ARS PEM翻译为六年级的阅读水平.
- 对比了原始和人工智能生成的PEM的可读性得分.
主要成果:
- 总共有164个PEM被评估,其中123个是由LLMs产生的.
- 与原始ARS PEM相比,人工智能生成的PEM显示出明显改善的可读性 (FKGL 8.6的平均值) (FKGL 10.28的平均值).
- 谷歌双子提供了最容易阅读的内容 (平均FKGL 7.5,FKRE 65.5).
结论:
- LLM提高了医疗患者教育材料的可读性,提高了不同人群的可访问性.
- 虽然LLM表现有前途,但在使用人工智能生成的内容来治疗特定疾病 (如鼻科) 时,建议谨慎使用.
- 需要进一步的研究来确保LLM产生的医疗信息的准确性和安全性.
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