The application of large language models in qualitative nursing research: A scoping review

Large language models (LLMs) provide significant potential benefits for nursing practice and research. Advanced natural language processing capabilities can effectively analyze text in qualitative studies. However, systematic exploration of their application contexts and efficacy in nursing remains limited.

This review aimed to conduct a scoping review of LLMs' application in qualitative nursing research, exploring current scenarios, effects, assessment methods, and challenges to provide a reference for future development.

Relevant literature was sourced from 11 databases up to April 2025 and used Joanna Briggs Scoping Review Methodology and PRISMA-ScR reporting standards. The search terms for this review included "nurs*," "large language model*," "qualitative study*," and "qualitative analys*."

We included 11 studies after reviewing 2,478 articles. The application scenarios of LLMs in qualitative nursing research included topic generation, role-playing, and interview question generation. LLM outputs demonstrated moderate-to-high similarity to human outputs in theme generation and superior text analysis efficiency but performed poorly in applying theoretical frameworks, generating interview questions, and developing codebooks.

This review systematically outlined LLMs applications and limitations in qualitative nursing research. Although LLM has great potential, its application is still in its infancy.

Future research needs to address issues such as analysis depth, simulation accuracy, technical limitations, and evaluation tools.