ArticleJMIR AI2025
Large Language Models for Thematic Summarization in Qualitative Health Care Research: Comparative Analysis of Model and Human Performance.
Article in JMIR AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Article
- What Provider Frequently Asked Questions Miss: Evaluating Unmet Attention-Deficit/Hyperactivity Disorder Information Needs Through Comparison of Online Community Posts Using Large Language Model-Assisted Semantic Analysis in a Mixed Methods Study.Journal of medical Internet research · 2026Article
- Utilizing BERTopic modeling for concept discovery in the domain of gerotranscendence and solitude.Journal of biomedical semantics · 2026Article
- Generative Artificial Intelligence for Qualitative Methods in Health Research: Rapid Review.Journal of medical Internet research · 2026Review
- Evidence, use cases, and implementation safeguards of large language models in primary care.Communications medicine · 2026Review
- Interpretable Topic Modeling of Spontaneous Speech in Depression Using Large Language Models: Multilingual Four-Cohort Study.JMIR mental health · 2026Article
- Walkable Intelligent Parks: Can a Large Language Model Turn Urban Park Audit Findings into Actionable Recommendations?International journal of environmental research and public health · 2026Article
- Advantages, limitations, and ethical concerns of AI-assisted qualitative research in nursing: insights from a human-AI comparative thematic analysis.BMC nursing · 2026Article
- "Do It by Myself" or Autonomy, Participation, and Assistive Devices and Technology Needs of Children and Youth With Disabilities: Text Mining Analysis of a National Survey in France.JMIR medical informatics · 2026Article
- Multimodule Human-Artificial Intelligence Collaboration Pipeline for Large Language Model-Assisted Thematic Analysis Across Digital Health Interview Studies: Comparative Evaluation Study.JMIR medical informatics · 2026Article
- Advanced Research Institute Scholars' Perspectives on Program Success: A Self-determination Theory Evaluation.The American journal of geriatric psychiatry : official journal of the American Association for Geriatric Psychiatry · 2026Article
- The use and methodological reporting of large language models in qualitative research: a scoping review.BMC medical research methodology · 2026Article
- Five Milestones to Addressing Rural Veteran Care Coordination, Care Management, and Case Management Needs: An Integrated Case Management Practice Framework.The Journal of rural health : official journal of the American Rural Health Association and the National Rural Health Care Association · 2026Article
- Large language models for thematic analysis in healthcare research: A blinded mixed-methods comparison with human analysts.PLOS digital health · 2026Article
- Foundations of systems-based hematology: thematic analysis of expert interviews to guide curriculums and promote growth.Blood advances · 2026Article
- Recalibrating academic expertise in the age of generative AI.Patterns (New York, N.Y.) · 2026Review
- Workplace Chinese training in a Chinese-managed factory in Morocco: a transfer-sensitive CIPP evaluation.Frontiers in psychology · 2026Article
- AI-assisted thematic synthesis of existing neurological core outcome sets: A descriptive reference framework (COS-Neuro).PloS one · 2026Article
- Automating inductive thematic analyses of health content using large language models: a proof-of-concept study using social media data.JAMIA open · 2025Article
- Article
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Authors and funding
5 authors.
Funding
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Abstract
Background: The application of large language models (LLMs) in analyzing expert textual online data is a topic of growing importance in computational linguistics and qualitative research within health care settings. Objective: The objective of this study was to understand how LLMs can help analyze expert textual data. Topic modeling enables scaling the thematic analysis of content of a large corpus of data, but it still requires interpretation. We investigate the use of LLMs to help researchers scale this interpretation. Methods: The primary methodological phases of this project were (1) collecting data representing posts to an online nurse forum, as well as cleaning and preprocessing the data; (2) using latent Dirichlet allocation (LDA) to derive topics; (3) using human categorization for topic modeling; and (4) using LLMs to complement and scale the interpretation of thematic analysis. The purpose is to compare the outcomes of human interpretation with those derived from LLMs. Results: There is substantial agreement (247/310, 80%) between LLM and human interpretation. For two-thirds of the topics, human evaluation and LLMs agree on alignment and convergence of themes. Furthermore, LLM subthemes offer depth of analysis within LDA topics, providing detailed explanations that align with and build upon established human themes. Nonetheless, LLMs identify coherence and complementarity where human evaluation does not. Conclusions: LLMs enable the automation of the interpretation task in qualitative research. There are challenges in the use of LLMs for evaluation of the resulting themes.
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