Registered nurses' experiences with generative artificial intelligence: a meta-synthesis of qualitative studies

To synthesize qualitative evidence on registered nurses' experiences of using generative artificial intelligence (GAI) in clinical practice and nursing research, and to identify perceived benefits, challenges, and support needs for its standardized implementation in nursing.

A qualitative meta-synthesis was conducted using the Joanna Briggs Institute meta-aggregation approach. PubMed, CINAHL, Embase, PsycINFO, Scopus, Web of Science, the Cochrane Library, CNKI, Wanfang, VIP, and the China Biomedical Literature Database were searched from inception to April 25, 2026. Two reviewers independently screened studies, extracted data, and assessed methodological quality using the JBI Critical Appraisal Checklist for Qualitative Research. Synthesized findings were assessed using the JBI ConQual approach.

Six qualitative studies involving 113 registered nurses were included. Thirty-eight findings were extracted and aggregated into eight categories, which generated three synthesized findings: (1) GAI may enhance work efficiency and professional competence; (2) nurses encounter ethical, cultural, and operational challenges when using GAI; and (3) nurses require training, institutional support, and clear guidance for standardized GAI use while maintaining positive expectations for future applications.

The available qualitative evidence suggests that registered nurses perceive GAI as a potentially supportive tool for improving efficiency, assisting clinical and research decision-making, and promoting professional development. However, the current evidence base remains limited, and the findings should be interpreted as preliminary. Further research across diverse healthcare systems and cultural contexts is needed to clarify how GAI can be safely and responsibly integrated into nursing practice.

https://www.crd.york.ac.uk/PROSPERO/view/CRD420261365306, Identifier CRD420261365306.