区分GPT生成的和人类写的反,用于放射科住院医生
Zier Zhou1, Arsalan Rizwan2, Nick Rogoza3
1School of Medicine, Queen's University, Kingston, ON, Canada.
Current problems in diagnostic radiology
|February 21, 2025
概括
像GPT-3.5这样的大型语言模型 (LLM) 难以为放射科住院医生与人类专家相比产生具体的,细微的反. 目前的LLM在区分人写和人工智能生成的教育评估方面也表现不佳.
科学领域:
- 医学教育 医学教育
- 医疗保健中的人工智能
- 放射学培训 放射学培训
背景情况:
- 加拿大放射学课程的基于能力的医学教育 (CBME) 需要增加教师评估.
- 大型语言模型 (LLM) 的扩散促使人们评估它们在为居民评估产生叙事反方面的有用性.
研究的目的:
- 为了比较人类的书面反与GPT-3.5生成的反为放射科住院医生.
- 评估人类评分员和GPT-3.5区分人类和人工智能生成的反的能力.
主要方法:
- 分析了来自加拿大诊断放射学计划的110名居民反意见.
- 使用GPT-3.5生成了110条合成评论,并与实际的人类评论混合.
- 两个教师评分员和GPT-3.5试图确定每个评论的来源.
主要成果:
- 人类反通常比GPT-3.5生成的评论更长,更具体.
- 当评论模糊时,区分人类和人工智能反更难.
- 人类评分器在源识别中获得了80.5%的准确性,而GPT-3.5只获得了50%的准确性.
结论:
- 目前,GPT-3.5还不足以满足人类专家在为放射科医生提供具体,细致的反方面的能力.
- 在区分真实反和合成反方面,GPT-3.5的能力低于人类.
- 结果可能会为开发先进的AI算法提供信息,以改善反生成和教师发展支持.
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