使用生成人工智能的可行性,用于虚拟患者的历史记录
1Department of Internal Medicine, College of Medicine, Dankook University, Cheonan, Korea.
BMC research notes
|February 25, 2025
概括
生成性AI聊天机器人为医学学生提供了一种可行的方法来练习患者病史记录. 虽然响应在相关性和准确性方面获得了很高的评分,但流性和自然性的改进是必要的.
科学领域:
- 医学教育 医学教育
- 医疗保健中的人工智能
背景情况:
- 医学学生需要有效的工具来练习患者病史的技能.
- 生成型人工智能 (AI) 提出了一种开发虚拟患者模拟器的新方法.
研究的目的:
- 设计和开发一个虚拟患者程序,利用生成AI进行病史记录实践.
- 在模拟的病史采集场景中评估AI产生的反应的可行性和质量.
主要方法:
- 使用Naver HyperCLOVA X®平台开发了一个虚拟患者聊天机器人.
- 五位专家审查员试点了该系统,参与了一个模拟的患者病史记录会议.
- 人工智能对答案的评估是使用五项利克特比例问卷和话语分析.
主要成果:
- 聊天机器人生成了96个问题答案对 (1325个单词).
- 话语分析发现2.6%的生成词是不可信的 (无关节,幻觉,缺失信息).
- 审稿人对AI反应的相关性 (4.50),有效性 (4.20) 和准确性 (4.10) 评价很高,但对流利性 (3.20) 显示中立.
结论:
- 使用生成AI进行虚拟患者病史记录是可行的.
- 需要进一步改进,以提高AI聊天机器人对医学教育的反应的表达性和自然性.
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