ArticleMedical science educator2026
Incorporating Artificial Intelligence in Qualitative Research: Exploring the Role of ChatGPT in Thematic Analysis.
Article in Medical science educator, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Large Language Model-Assisted Thematic Coding in Medical Education Research: Comparative Methodological Study.JMIR formative research · 2026Article
- AI in Scholarship: How to Use It, Study It, and Report It.Journal of graduate medical education · 2026Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Thematic analysis is a form of qualitive analysis performed to identify patterns within text-based datasets such as open-ended responses. While thematic analysis is used extensively in medical education research, it has several limitations, such as subjective interpretation by graders and the time required to manually code responses in large datasets. There is potential to overcome many of these challenges with the use of Artificial Intelligence (AI) platforms, such as the free to use large language model, ChatGPT. The goal of this study was to evaluate whether AI can be used in thematic analysis to replace manual graders as the gold standard. The dataset used in this study was related to first year medical students' thoughts and feelings regarding the act of cadaveric dissections. Three different methods were used to instruct the AI to grade the responses, and each method was repeated three times. Various measures related to precision and accuracy were compared, both within the repeated tests using AI and between the AI generated results and those obtained by manual coders. Results show that Method 3 had greater accuracy and agreement with the manual coders, but less precision compared to the other two methods. All methods had an agreement greater than 80%. These findings demonstrate that AI has promise in being used for thematic analysis, but the method used to instruct the AI has a strong influence on the results. When using AI for thematic analyses, it is imperative to carefully document and refine the methodology as well as still incorporate some human oversight to ensure accurate results. Supplementary Information: The online version contains supplementary material available at 10.1007/s40670-025-02587-2.
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Registered trials
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