Cook David A, Ginsburg Shiphra, Sawatsky Adam P, Kuper Ayelet, D'Angelo Jonathan D
Acad Med. 2025 Jun 24. doi: 10.1097/ACM.0000000000006134.
How can artificial intelligence (AI) be used to support qualitative data analysis (QDA)? To address this question, the authors conducted 3 scholarly activities. First, they used a readily available large language model, ChatGPT-4, to analyze 3 existing narrative datasets (February 2024). ChatGPT generated accurate brief summaries; for all other attempted tasks the initial prompt failed to produce desired results. After iterative prompt engineering, some tasks (e.g., keyword counting, summarization) were successful, whereas others (e.g., thematic analysis, keyword highlighting, word tree diagram, cross-theme insights) never generated satisfactory results. Second, the authors conducted a brief scoping review of AI-supported QDA (through May 2024). They identified 130 articles (104 original research, 26 nonresearch) of which 64 were published in 2023 or 2024. Seventy studies inductively analyzed data for themes, 39 used keyword detection, 30 applied a coding rubric, 28 used sentiment analysis, and 13 applied discourse analysis. Seventy-five used unsupervised learning (e.g., transformers, other neural networks). Third, building on these experiences and drawing from additional literature, the authors examined the potential capabilities, shortcomings, dangers, and ethical repercussions of AI-supported QDA. They note that AI has been used for QDA for more than 25 years. AI-supported QDA approaches include inductive and deductive coding, thematic analysis, computational grounded theory, discourse analysis, analysis of large datasets, preanalysis transcription and translation, and suggestions for study planning and interpretation. Concerns include the imperative of a "human in the loop" for data collection and analysis, the need for researchers to understand the technology, the risk of unsophisticated analyses, inevitable influences on workforce, and apprehensions regarding data privacy and security. Reflexivity should embrace both strengths and weaknesses of AI-supported QDA. The authors conclude that AI has a long history of supporting QDA through widely varied methods. Evolving technologies make AI-supported QDA more accessible and introduce both promises and pitfalls.
人工智能（AI）如何用于支持定性数据分析（QDA）？为解决这个问题，作者开展了三项学术活动。首先，他们使用了一个现成的大语言模型ChatGPT-4来分析三个现有的叙事数据集（2024年2月）。ChatGPT生成了准确的简短摘要；对于所有其他尝试的任务，初始提示未能产生预期结果。经过迭代提示工程，一些任务（如关键词计数、摘要）取得了成功，而其他任务（如主题分析、关键词突出显示、词树图、跨主题见解）从未产生令人满意的结果。其次，作者对人工智能支持的定性数据分析进行了简要的范围审查（截至2024年5月）。他们确定了130篇文章（104篇原创研究、26篇非研究），其中64篇发表于2023年或2024年。70项研究对数据进行归纳主题分析，39项使用关键词检测，30项应用编码规则，28项使用情感分析，13项应用话语分析。75项使用无监督学习（如变压器、其他神经网络）。第三，基于这些经验并借鉴其他文献，作者研究了人工智能支持的定性数据分析的潜在能力、缺点、风险和伦理影响。他们指出，人工智能已用于定性数据分析超过25年。人工智能支持的定性数据分析方法包括归纳和演绎编码、主题分析、计算扎根理论、话语分析、大型数据集分析、预分析转录和翻译，以及研究规划和解释建议。关注点包括数据收集和分析中“人工介入”的必要性、研究人员了解该技术的需求、简单分析的风险、对劳动力的不可避免影响，以及对数据隐私和安全的担忧。反思应兼顾人工智能支持的定性数据分析的优点和缺点。作者得出结论，人工智能通过广泛多样的方法支持定性数据分析已有很长历史。不断发展的技术使人工智能支持的定性数据分析更容易获得，同时带来了机遇和陷阱。
