# Harnessing ChatGPT for thematic analysis: Are we ready?

arXiv: https://arxiv.org/abs/2310.14545 · HTML: https://mlsanity.com/paper/2310.14545/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 23 Oct 2023.

## Builds on (most cited first, 4 of 4)

- [Supporting Qualitative Analysis with Large Language Models: Combining Codebook with GPT-3 for Deductive Coding](https://mlsanity.com/paper/2304.10548/) (2023)
- [CollabCoder: A Lower-barrier, Rigorous Workflow for Inductive Collaborative Qualitative Analysis with Large Language Models](https://mlsanity.com/paper/2304.07366/) (2023)
- [Can Large Language Models emulate an inductive Thematic Analysis of semi-structured interviews? An exploration and provocation on the limits of the approach and the model](https://mlsanity.com/paper/2305.13014/) (2023)
- [Writing user personas with Large Language Models: Testing phase 6 of a Thematic Analysis of semi-structured interviews](https://mlsanity.com/paper/2305.18099/) (2023)

## Built on by (most cited first, 8 of 8)

- [Auto-TA: Towards Scalable Automated Thematic Analysis (TA) via Multi-Agent Large Language Models with Reinforcement Learning](https://mlsanity.com/paper/2506.23998/) (2025)
- [RACER: An LLM-powered Methodology for Scalable Analysis of Semi-structured Mental Health Interviews](https://mlsanity.com/paper/2402.02656/) (2024)
- [Exploring the Human-LLM Synergy in Advancing Theory-driven Qualitative Analysis](https://mlsanity.com/paper/2405.05758/) (2024)
- [Can large language models interpret unstructured chat data on dynamic group decision-making processes? Evidence on joint destination choice](https://mlsanity.com/paper/2601.05582/) (2026)
- [Medalyze: Lightweight Medical Report Summarization Application Using FLAN-T5-Large](https://mlsanity.com/paper/2505.17059/) (2025)
- [Not Everything That Counts Can Be Counted: A Case for Safe Qualitative AI](https://mlsanity.com/paper/2511.09325/) (2025)
- [Designing Computational Tools for Exploring Causal Relationships in Qualitative Data](https://mlsanity.com/paper/2602.06506/) (2026)
- [How AI Coders Discuss, Disagree, and Reach Consensus: Challenges and Opportunities for LLM-Based Qualitative Coding](https://mlsanity.com/paper/2609.11109/) (2026)

## 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.