Generative Artificial Intelligence for Qualitative Methods in Health Research: Rapid Review

Generative AI (GAI) is rapidly transforming research practices, including qualitative methods in health research. While these tools offer efficiency in processing large volumes of textual data, concerns remain regarding their methodological rigor, interpretive capacity, equity, and ethical implications.

This rapid review aimed to synthesize the current evidence on the use of GAI in health-related qualitative research, focusing on its applications, performance relative to human analysis, and implications for rigor, ethics, and equity.

We conducted a rapid review following Joanna Briggs Institute and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Peer-reviewed studies published between 2022 and December 2025 were identified through searches in PubMed, Web of Science, and Scopus. Eligible studies included qualitative or mixed methods research that used GAI tools (eg, ChatGPT, Gemini, and Claude) during qualitative analysis, including studies that compared GAI-generated outputs with human researchers, coders, or traditional qualitative analytic approaches. Data were extracted using a structured template and synthesized descriptively. Study quality was assessed using the Critical Appraisal Skills Programme (CASP) checklist. This rapid review was registered with the International Prospective Register of Systematic Reviews (PROSPERO; CRD420261280832). The review adhered to the registered PROSPERO protocol; no deviations occurred.

A total of 42 studies met the inclusion criteria; 71.4% (n=30) were published in 2025, and 81% (n=34) used qualitative designs. Thematic analysis (n=20, 40%) and content analysis (n=10, 20%) were the most common qualitative approaches. GAI was most applied during data familiarization, coding, and theme development, with ChatGPT being the most frequently reported GAI, accounting for nearly two-thirds of all model occurrences (n=37, 62.7%). Among studies evaluating GAI performance relative to human qualitative analysis, performance was strongest in inductive thematic and content analyses, with agreement often exceeding 80% for descriptive themes but dropping to approximately 30% for culturally nuanced themes. Several studies reported time to complete analyses up to 97% faster than human-led analyses. However, performance declined for reflexive and theory-driven analyses, particularly when interpreting culturally nuanced or emotionally complex data. Across studies, GAI improved efficiency but frequently produced superficial interpretations, misapplied theoretical frameworks, and generated occasional inaccuracies, including fabricated quotes. Human oversight was consistently identified as essential to ensure validity, contextual accuracy, and ethical integrity. Concerns related to bias, transparency, and data privacy were widely reported.

GAI can effectively support early-stage qualitative analysis and enhance efficiency in health research; however, it cannot replace the interpretive and reflexive functions central to qualitative inquiry. A hybrid human-AI approach is recommended, in which GAI assists with data processing while researchers retain responsibility for interpretation, contextualization, and ethical oversight. Future research should prioritize developing guidelines that address equity, transparency, and responsible integration of GAI into qualitative methodologies.