人工智能能读取字幕吗:利用ChatGPT评估医学学生对医生与病人关系的隐含态度
Wenqi Geng1, Yinan Jiang1, Wei Zhai2
1Department of Psychological Medicine, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Medical teacher
|June 11, 2025
概括
在评估医学学生对医生与患者关系 (DPR) 的隐含态度方面,ChatGPT显示出有前途. 虽然注释的部分比专家少,但它的分级准确性和与人类判断的一致性表明,LLM可以帮助评估DPR技能.
科学领域:
- 医学教育 医学教育
- 医疗保健中的人工智能
- 心理评估 心理评估
背景情况:
- 评估医学教育中的隐性态度对于有效的医生与患者关系 (DPR) 至关重要.
- 评估这些态度的传统方法可能是耗时和主观的.
- 大型语言模型 (LLM) 的出现为客观评估提供了潜在的新途径.
研究的目的:
- 调查ChatGPT的实用性,一个复杂的LLM,在评估医学学生对医生与患者关系 (DPR) 的隐含态度.
- 将ChatGPT的表现与人类专家和学生自我评估进行比较,以评分DPR态度.
主要方法:
- 分析了10名医学学生的面试成绩单.
- 将隐含的DPR态度分类为5个层次的关怀和分享维度.
- 评估ChatGPT识别DPR相关内容和等级态度的能力,然后对其性能进行专家评估.
主要成果:
- 与人类专家相比,ChatGPT发现了较少的DPR相关细分.
- 专家和ChatGPT的课程前评分是可比的,但低于自我评估.
- 在课程结束后,ChatGPT的分数超过了专家的分数,并且仍然高于自我评估.
- 在态度分类中,ChatGPT实现了高准确度 (0.84-0.85),精度 (0.81-0.85),回忆 (0.84-0.85) 和F1分数 (0.82-0.84),平均可接受度为3.9/5.5.
结论:
- 在评估隐含态度方面,LLM与人类专家具有显著的一致性.
- 聊天GPT的表现表明它作为评估医生和患者关系技能在医学教育中的工具的潜力.
- 建议进行进一步的研究,以优化LLMs,并在不同的环境中与更大的参与者群体验证这一框架.
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