JMIR Publications

ABSTRACT

Background:

Generative artificial intelligence (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.

Objective:

This rapid review aims 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.

Methods:

We conducted a rapid review following JBI and PRISMA 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 (e.g., ChatGPT, Gemini, Claude) at least once in the qualitative analysis. Data were extracted using a structured template and synthesized descriptively. Study quality was assessed using the Critical Appraisal Skills Programme (CASP) checklist. The review is registered with PROSPERO (CRD420261280832).

Results:

A total of 42 studies met the inclusion criteria. GAI was most commonly applied during data familiarization, coding, and theme development. Performance was strongest in inductive thematic and content analysis, with agreement with human analysts often exceeding 80% for descriptive themes. 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.

Conclusions:

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.