mlsanityData to 7 Oct 2026

Harnessing ChatGPT for thematic analysis: Are we ready?

arXiv 2310.14545 ↗First posted 23 Oct 2023cs.CLcited by 8

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Abstract (arXiv)

ChatGPT 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 utilization 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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 4 of its 28 references that are in the map. First posted on arXiv 23 Oct 2023.

Builds on

4 of 28 references.

References · 4

  1. 2023Supporting Qualitative Analysis with Large Language Models: Combining Codebook with GPT-3 for Deductive Codingcited by 82
  2. 2023CollabCoder: A Lower-barrier, Rigorous Workflow for Inductive Collaborative Qualitative Analysis with Large Language Modelscited by 34
  3. 2023Can Large Language Models emulate an inductive Thematic Analysis of semi-structured interviews? An exploration and provocation on the limits of the approach and the modelcited by 15
  4. 2023Writing user personas with Large Language Models: Testing phase 6 of a Thematic Analysis of semi-structured interviewscited by 7

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 3; 2026: 3.

8 papers.

  1. 2025Auto-TA: Towards Scalable Automated Thematic Analysis (TA) via Multi-Agent Large Language Models with Reinforcement Learningcited by 9
  2. 2024RACER: An LLM-powered Methodology for Scalable Analysis of Semi-structured Mental Health Interviewscited by 6
  3. 2024Exploring the Human-LLM Synergy in Advancing Theory-driven Qualitative Analysiscited by 2
  4. 2026Can large language models interpret unstructured chat data on dynamic group decision-making processes? Evidence on joint destination choicecited by 1
  5. 2025Medalyze: Lightweight Medical Report Summarization Application Using FLAN-T5-Largecited by 0
  6. 2025Not Everything That Counts Can Be Counted: A Case for Safe Qualitative AIcited by 0
  7. 2026Designing Computational Tools for Exploring Causal Relationships in Qualitative Datacited by 0
  8. 2026How AI Coders Discuss, Disagree, and Reach Consensus: Challenges and Opportunities for LLM-Based Qualitative Codingcited by 0