人工智能为结性脊柱炎患者提供患者教育:可读性和质量的比较研究
Neşe Çabuk Çelik1, Elif Altunel Kılınç2
1Rheumatology Department, Sincan Training and Research Hospital, Ankara, Turkey. nesecabuk@gmail.com.
Clinical rheumatology
|December 14, 2025
概括
来自像ChatGPT-4o和DeepSeek V3这样的AI聊天机器人的结性脊髓炎 (AS) 患者教育材料显示了高质量但复杂的语言. 未来的人工智能工具需要更简单的术语和引文,以便更好地了解患者.
科学领域:
- 医疗保健中的人工智能
- 医疗教育 技术 技术 医学教育
- 风湿病患者信息 风湿病患者信息
背景情况:
- 患者教育材料 (PEM) 对于管理慢性疾病,如结脊髓炎 (AS) 至关重要.
- 大型语言模型 (LLM) 为生成可访问的健康信息提供了潜力.
- 评估人工智能生成的PEM的质量和可读性对于临床实用性至关重要.
研究的目的:
- 为了比较四个领先的AI大语言模型 (LLM) 产生的结性脊髓炎患者教育材料的质量和可读性.
- 评估人工智能产生的内容是否适合于不同教育背景的患者理解.
主要方法:
- 关于结性脊柱炎的10个经常在谷歌上搜索的问题被提出给ChatGPT-4o,ChatGPT-3.5,DeepSeek R1和DeepSeek V3.
- 内容质量通过两个独立的风湿病学家使用DISCERN工具进行评估.
- 可读性通过Flesch阅读易度得分 (FRES) 和Flesch-Kincaid等级水平 (FKGL) 进行评估.
主要成果:
- 聊天GPT-4o获得了最高的DISCERN质量评分 (72.38),表明内容准确度优越.
- DeepSeek V3 显示了最高的 Flesch 阅读易度得分 (14.93),表明更好的可理解性.
- 所有模型都产生了需要大学水平阅读技能的内容 (FKGL 15.33-15.93),这表明了一般患者群体的复杂性.
结论:
- 人工智能为结性脊柱炎生成的患者教育材料信息密集和复杂,主要适用于受过高等教育的患者.
- 虽然内容质量很高,但LLMs需要优化清晰度和可理解性,以满足各种患者需求.
- 未来的人工智能开发应该专注于简化语言和结合基于证据的引用,以提高临床适用性和公共利益.
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