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A mixed-methods study comparing human-led and ChatGPT-driven qualitative analysis in medical education research
Takeshi Kondo1,2, Junichiro Miyachi2,3, Anders Jönsson4
1Department of General Medicine/Family & Community Medicine, Nagoya University Graduate School of Medicine, Nagoya, Japan.
Nagoya Journal of Medical Science
|January 9, 2025
Summary
This study explored using ChatGPT for thematic analysis in medical education research. While ChatGPT shows promise, human oversight is crucial for in-depth, context-rich qualitative analysis.
Area of Science:
- Medical Education
- Artificial Intelligence in Research
Background:
- Qualitative research is vital for understanding medical education but is time-consuming.
- Technology, like large language models, offers potential for streamlining qualitative data analysis.
Purpose of the Study:
- To evaluate the applicability of ChatGPT (GPT-4) in performing thematic analysis for medical qualitative research.
- To compare ChatGPT's inductive thematic analysis capabilities against human qualitative analysis.
Main Methods:
- A convergent design mixed-methods study was employed.
- ChatGPT (GPT-4) analyzed interview data from a published medical research article using a thematic analysis approach.
- Three assessors compared ChatGPT's analysis with human-conducted analysis.
Main Results:
- ChatGPT demonstrated strengths in data extraction and summarization, often achieving superficial similarity to human analysis.
- However, ChatGPT exhibited variable transferability and mixed depth scores, with identified themes including prompt contamination and lack of theoretical derivation.
- Assessors noted ChatGPT's tendency towards "thick descriptions" deficiency and context-based limitations.
Conclusions:
- ChatGPT can assist in qualitative data analysis but requires careful prompt engineering and human scrutiny to mitigate issues like prompt contamination.
- Human input remains essential for providing research context, theoretical depth, and ensuring comprehensive analysis in medical qualitative research.
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