人工智能大语言模型 (LLM) 的表现在回答有关异二胺素的常见问题
1Dermatology Department, Gaziantep Emek Hospital, Gaziantep, Türkiye.
Cutaneous and ocular toxicology
|December 18, 2025
概括
像ChatGPT,Copilot和Gemini这样的AI聊天机器人提供了关于异丁的准确信息,但在患者教育中难以阅读和使用. 需要进行改进,以简化人工智能反应,以便更好地了解患者.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 皮肤病学 皮肤病学
背景情况:
- 人工智能 (AI) 聊天机器人越来越多地用于健康信息检索.
- 评估人工智能产生的健康内容的质量对于患者安全和教育至关重要.
研究的目的:
- 评估ChatGPT,Copilot和Gemini关于异丁因的反应的准确性,可读性,适用性和可理解性.
- 为了比较领先的人工智能聊天机器人在提供皮肤学信息方面的表现.
主要方法:
- 分析了ChatGPT,Copilot和Gemini对与异三类素相关问题的反应.
- 使用弗莱什-金凯德易度得分来评估可读性.
- 使用患者教育材料评估工具评估了适用性和可理解性.
- 两个皮肤科医生对准确度进行了评分.
主要成果:
- 所有的人工智能程序都表现出难以阅读的难度水平 (弗莱什-金凯德分数).
- 双子座和聊天GPT显示了高的适用性和可理解性 (>70%在PET工具),显著优于Copilot.
- 双子座 (4.90 ± 0.31) 和ChatGPT (4.60 ± 0.69) 实现了高准确度得分,但观察到显著差异.
结论:
- 人工智能聊天机器人对异丁信息具有很好的准确性,但在可读性和可用性方面存在局限性.
- 当前人工智能产生的内容需要简化才能有效地为患者教育.
- 需要进一步开发,以加强人工智能在患者教育材料中的实际应用.
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