人类分析与人类分析. 人工智能:分析医学学生的定性叙述
Kinneret Misgav1, Galit Neufeld-Kroszynski2, Michal Palombo2,3
1Data Research Unit, Hadassah Research Fund, Hadassah Medical Center, Jerusalem, Israel.
Qualitative health research
|August 7, 2025
概括
大型语言模型 (LLM) 可以有效地处理医学学生的叙述,以获取上下文数据. 然而,他们与微妙的解释作斗争,表明LLMs可以补充,而不是取代,人类的定性分析.
科学领域:
- 医学教育 医学教育
- 人工智能的人工智能
- 定性研究是指质量研究.
背景情况:
- 医学学生的反思性叙述提供了丰富的关于经验的定性数据.
- 手动分析这些叙述是耗时且昂贵的.
- 人工智能 (AI) 工具,特别是大型语言模型 (LLM),显示了简化分析的潜力.
研究的目的:
- 探索LLMs在分析医学学生叙事中的有效性.
- 将LLM分析与手动定性分析进行比较.
- 在敏感的医疗遭遇叙事的上下文和主题分析中评估LLM绩效.
主要方法:
- 利用LLM来分析医学学生反映坏消息传递的半结构化叙述.
- 将LLM衍生分析与之前进行的手动分析进行比较.
- 评估了跨背景 (人口统计) 和主题类别的绩效.
主要成果:
- 法律法规成功处理了叙事数据,并遵守了格式化要求.
- 法学士的成绩各不相同;在简单的类别 (如年龄) 中表现强,在微妙的方面表现弱 (如惊喜).
- 叙述的模糊性和复杂性对LLM的解释准确性提出了挑战.
结论:
- 通过提取明确的上下文信息,LLM可以提高叙事分析的效率.
- 作为人类定性研究人员的补充工具,LLM显示出希望.
- 需要进一步的研究来完善LLM的复杂叙事解释和人与人工智能的协同分析能力.
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