SynthesisJournal of health, population, and nutrition2026
Reimagining nursing practice in the era of AI: a qualitative systematic review and meta-synthesis of nurses' lived experiences.
Synthesis in Journal of health, population, and nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe 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.
methodsFollowing 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.
resultsFive 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.
conclusionAI 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.