This paper is in the following e-collection/theme issue:

Applications of AI (273) Generative Language Models Including ChatGPT (674) Natural Language Processing (894) Development and Evaluation of Research Methods, Instruments and Tools (729) Digital Health Reporting Standards, Quality and Transparency in e-Research (129) Machine Learning (2036) Focus Groups and Qualitative Research for Human Factors Research (1188) Nursing (129)

Published on in Vol 4 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/64447, first published .
Large Language Models for Thematic Summarization in Qualitative Health Care Research: Comparative Analysis of Model and Human Performance

Large Language Models for Thematic Summarization in Qualitative Health Care Research: Comparative Analysis of Model and Human Performance

Large Language Models for Thematic Summarization in Qualitative Health Care Research: Comparative Analysis of Model and Human Performance

Authors of this article:

Arturo Castellanos1 Author Orcid Image ;   Haoqiang Jiang2 Author Orcid Image ;   Paulo Gomes3 Author Orcid Image ;   Debra Vander Meer3 Author Orcid Image ;   Alfred Castillo3 Author Orcid Image
Article Authors Cited by (7) Tweetations Metrics

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  2. Fehring L, Frings J, Rust P, Kempny C, Thürmann P, Meister S. Extension of the Consolidated Criteria for Reporting Qualitative Research Guideline to Large Language Models (COREQ+LLM): Protocol for a Multiphase Study. JMIR Research Protocols 2025;14:e78682 View
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  5. Hairston J, Ranjan R, Lakamana S, Spadaro A, Bozkurt S, Perrone J, Sarker A. Automating inductive thematic analyses of health content using large language models: a proof-of-concept study using social media data. JAMIA Open 2025;8(5) View
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