使用人工智能驱动的主题分析:方法,实施和经验教训
Arif A Cevik1, Fikri M Abu-Zidan2
1Department of Internal Medicine, Emergency Medicine Section, College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, ARE.
Cureus
|July 7, 2025
概括
人工智能 (AI) 可以通过大语言模型 (LLM) 增强定性研究. 人类监督对于完善人工智能输出和确保人工智能辅助分析的方法严谨性至关重要.
科学领域:
- 医学研究 医学研究
- 定性分析是一种定性分析.
- 人工智能应用的人工智能应用.
背景情况:
- 大型语言模型 (LLM) 显示了自动化定性研究任务 (如数据分析和编码) 的潜力.
- 现有的关于LLM在定性研究中的表现的研究缺乏明确的实施指南.
- 人工智能 (AI) 准备彻底改变医疗保健,教育和研究方法.
研究的目的:
- 提供实用,逐步的方法和提示,用于在定性分析中实施LLMs.
- 根据主题分析框架开发和评估一个定制的生成预训练变压器 (Custom-GPT).
- 评估LLM生成的主题与手动编码的一致性和一致性.
主要方法:
- 开发了一个Custom-GPT模型,与布朗和克拉克的六步主题分析框架保持一致.
- 在三个不同的数据集中评估了Custom-GPT模型的性能.
- 使用谷歌的NotebookLM进行三角测量和与手动编码和主题解释进行比较.
主要成果:
- 定制-GPT模型产生了一致的主题结构,与数据集中的手动编码密切匹配.
- 确定了一些挑战,包括响应的变化,人工智能决策缺乏透明度,以及需要反复提示.
- 在步骤之间需要人类干预,以改进输出并保持方法的完整性.
结论:
- 在提高定性主题分析效率方面,LLM提供了显著的机会.
- 人类监督对于解决LLM的局限性和确保AI辅助定性研究的可靠性至关重要.
- 需要对道德框架,特定领域的LLM和负责的人工智能集成的高级提示工程进行进一步的研究.
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