Generative AI for thematic analysis in a maternal health study: coding semistructured interviews using large language models.

Journal: Applied psychology. Health and well-being
Published Date: Jun 1, 2025

Abstract

STUDY OBJECTIVES: The coding of semistructured interview transcripts is a critical step for thematic analysis of qualitative data. However, the coding process is often labor-intensive and time-consuming. The emergence of generative artificial intelligence (GenAI) presents new opportunities to enhance the efficiency of qualitative coding. This study proposed a computational pipeline using GenAI to automatically extract themes from interview transcripts.

Authors

  • Shan Qiao
    Department of Health Promotion, Education and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, SC, United States.
  • Xingyu Fang
    GISense Lab, Department of Geography and the Environment, The University of Texas at Austin, Austin, Texas, USA.
  • Junbo Wang
    State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People's Republic of China.
  • Ran Zhang
    Jiangsu Province Key Laboratory of Drug Metabolism and Pharmacokinetics, China Pharmaceutical University, Nanjing, Jiangsu, 210009, China.
  • Xiaoming Li
    Department of Radiology, Southwest Hospital, Third Military Medical University (Army Military Medical University), Chongqing, China.
  • Yuhao Kang
    Geospatial Data Science Lab, Department of Geography, University of Wisconsin, Madison, WI, United States of America.

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