ArticleJournal of medical Internet research2024
Harnessing ChatGPT for Thematic Analysis: Are We Ready?
Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 43 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
43 citing papers in PubMed.
- Article
- Stakeholder Perspectives on Facilitators of and Barriers to the Implementation of Internet of Things-Equipped Powered Wheelchairs in Sweden: Qualitative Case Study.JMIR rehabilitation and assistive technologies · 2026Article
- Large Language Model-Assisted Thematic Coding in Medical Education Research: Comparative Methodological Study.JMIR formative research · 2026Article
- Advantages, limitations, and ethical concerns of AI-assisted qualitative research in nursing: insights from a human-AI comparative thematic analysis.BMC nursing · 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
- The use and methodological reporting of large language models in qualitative research: a scoping review.BMC medical research methodology · 2026Article
- Feasibility and Acceptability of A Self-Directed Virtual Interview Preparation Tool for Medical Students.Medical science educator · 2026Article
- Integrating Large Language Models into Qualitative Methods in Health Services Research: A Proof-of-Concept Study.Research square · 2026Article
- Falls prevention for older adults in outdoor public spaces: an interdisciplinary Delphi consensus on risks, actions, and barriers.BMC public health · 2026Article
- Using large language models to assist qualitative thematic analysis of student reflections on advance care planning education.BMC medical education · 2026Article
- Query-Based Analysis: A Strategy for Analyzing Qualitative Data Using ChatGPT.Qualitative health research · 2026Article
- Toward a single source of truth: The case for centralized nutrition documentation in electronic medical records.JPGN reports · 2026Article
- Recalibrating academic expertise in the age of generative AI.Patterns (New York, N.Y.) · 2026Review
- Medical challenges and unmet needs of individuals with 22q11.2 deletion syndrome as perceived by caregivers: A thematic analysis and natural language processing-based thematic extraction.PLOS mental health · 2026Article
- AI-assisted thematic synthesis of existing neurological core outcome sets: A descriptive reference framework (COS-Neuro).PloS one · 2026Article
- Exploring Personal Experience and Value Creation in Postdigital Education: Insights from a Large-Scale MOOC Survey.Postdigital science and education · 2026Article
- A primer on artificial intelligence for palliative care educators.Palliative care and social practice · 2026Review
- A structured framework for effective and responsible generative artificial intelligence chatbot prompt engineering throughout the scientific process: a comprehensive guide for the health and medical researcher.Frontiers in artificial intelligence · 2026Review
- A framework for generative AI-driven extraction of clinical user needs in pediatric device development.Frontiers in digital health · 2026Article
- Integrating patient-reported weight gain cause narratives into personalized obesity management: a data-driven approach with natural language processing and machine learning.Frontiers in nutrition · 2026Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
ChatGPT (OpenAI) is an advanced natural language processing tool with growing applications across various disciplines in medical research. Thematic analysis, a qualitative research method to identify and interpret patterns in data, is one application that stands to benefit from this technology. This viewpoint explores the use of ChatGPT in three core phases of thematic analysis within a medical context: (1) direct coding of transcripts, (2) generating themes from a predefined list of codes, and (3) preprocessing quotes for manuscript inclusion. Additionally, we explore the potential of ChatGPT to generate interview transcripts, which may be used for training purposes. We assess the strengths and limitations of using ChatGPT in these roles, highlighting areas where human intervention remains necessary. Overall, we argue that ChatGPT can function as a valuable tool during analysis, enhancing the efficiency of the thematic analysis and offering additional insights into the qualitative data. While ChatGPT may not adequately capture the full context of each participant, it can serve as an additional member of the analysis team, contributing to researcher triangulation through knowledge building and sensemaking.
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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.