ArticleEye (London, England)2025
Unravelling ChatGPT's potential in summarising qualitative in-depth interviews.
Article in Eye (London, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluating Large Language Models in Ophthalmology: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Generative Artificial Intelligence for Qualitative Methods in Health Research: Rapid Review.Journal of medical Internet research · 2026Review
- The use and methodological reporting of large language models in qualitative research: a scoping review.BMC medical research methodology · 2026Article
- How Far Have Large Language Models Advanced in Ophthalmology? A Systematic Review of Their Development, Evaluation, and Readiness for Clinical Use.Research square · 2026Article
- Using machine-assisted topic analysis to expedite thematic analysis of free-text data: Exemplar investigation of factors influencing health behaviours and wellbeing during the COVID-19 pandemic.British journal of health psychology · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
BACKGROUND/
objectivesQualitative research can be laborious and time consuming, presenting a challenge for practitioners and policymakers seeking rapid, actionable results. Data collection, transcription and analysis are the main contributors to the resource-intensive nature. OpenAI's Chat Generative Pre-trained Transformer (ChatGPT), have demonstrated potential to aid in data analysis. Our study aimed to compare themes generated by ChatGPT (3.5 and 4.0) with traditional human analysis from in-depth interviews.
methodsThree transcripts from an evaluation study to understand patients' experiences at a community eye clinic were used. Transcripts were first analysed by an independent researcher. Next, specific aims, instructions and de-identified transcripts were uploaded to ChatGPT 3.5 and ChatGPT 4.0. Concordance in the themes was calculated as the number of themes generated by ChatGPT divided by the number of themes generated by the researcher. The number of unrelated subthemes and time taken by both ChatGPT were also described.
resultsThe average time taken per transcript was 11.5 min, 11.9 min and 240 min for ChatGPT 3.5, ChatGPT 4.0 and researcher respectively. Six themes were identified by the researcher: (i) clinic's accessibility, (ii) patients' awareness, (iii) trust and satisfaction, (iv) patients' expectations, (v) willingness to return and (vi) explanation of the clinic by referral source. Concordance for ChatGPT 3.5 and 4.0 ranged from 66 to 100%.
conclusionPreliminary results showed that ChatGPT significantly reduced analysis time with moderate to good concordance compared with current practice. This highlighted the potential adoption of ChatGPT to facilitate rapid preliminary analysis. However, regrouping of subthemes will still need to be conducted by a researcher.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.