Reimagining nursing practice in the era of AI: a qualitative systematic review and meta-synthesis of nurses’ lived experiences

INTRODUCTION: The integration of artificial intelligence (AI) into healthcare is transforming nursing practice, introducing both opportunities and challenges. This qualitative systematic review and meta-synthesis examines nurses' encounters with AI technologies across clinical, managerial, and educational domains. It aims to provide a comprehensive understanding of nurses' lived experiences with AI technologies across clinical, managerial, and educational domains, identify barriers and facilitators to its integration, and derive an interpretive framework for nurses' positioning in AI-enhanced care environments. METHODS: Following PRISMA and ENTREQ guidelines, a comprehensive literature search was conducted across PubMed, CINAHL, Scopus, Web of Science, and Embase for peer-reviewed qualitative studies from January 2017 to June 2025. The SPIDER framework defined eligibility, focusing on nurses' experiences with AI. Two reviewers independently screened 284 deduplicated records, with 26 studies included after full-text review. Data were extracted using a tailored form, and quality was assessed via the CASP checklist. A combined meta-ethnography and thematic analysis synthesized findings, generating themes through iterative coding and consensus. Verbatim quotes ensured fidelity to nurses' voices, with methodological rigor maintained through reflexivity and member checking. RESULTS: Five themes emerged from 26 studies: (1) Enhancing Clinical Efficiency and Decision-Making, where AI improves risk prediction and workflows; (2) Navigating Barriers to AI Integration, highlighting technical and organizational challenges; (3) Ethical and Cultural Considerations, emphasizing patient autonomy and bias concerns; (4) Evolving Nursing Roles, reflecting shifts to supervisory and technical competencies; and (5) AI's Role in Enhancing Communication, noting its facilitation and depersonalization risks. Nurses value AI's efficiency but stress user-friendly design and ethical safeguards. Continuous training is needed to balance technical skills with empathy. CONCLUSION: AI significantly enhances nursing efficiency and decision-making but introduces technical, ethical, and role-related challenges. User-centered AI design, comprehensive training, and ethical frameworks are essential to address barriers and biases. Nurses' evolving roles require balancing technical proficiency with humanistic care. Future research should explore longitudinal impacts to ensure AI supports equitable, patient-centered nursing practice.