ReviewSAGE open nursing
Artificial Intelligence in Nursing Governance and Regulation: An Umbrella Review of Ethical and Policy Dimensions.
Review in SAGE open nursing. 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: As AI becomes increasingly embedded in healthcare systems, nursing governance faces new challenges involving ethical accountability, professional autonomy, data stewardship, and institutional oversight. Existing reviews highlight fragmented understanding of how these changes impact the nursing profession. Aim: This umbrella review aimed to synthesize ethical and policy dimensions related to the integration of artificial intelligence (AI) within nursing governance and regulatory frameworks. Methods: Following JBI guidance and PRISMA 2020, five databases were searched for reviews published from January 2010 to December 2025. Reviews were appraised and synthesized by purpose, quality, nursing specificity, and primary study overlap, which was quantified using a citation matrix and Corrected Covered Area (CCA). Findings: Thirty-one reviews included 23 evidence syntheses and eight evidence maps. Privacy or data stewardship appeared in 29 reviews, transparency or explainability in 27, bias or fairness in 25, accountability or liability in 25, consent or autonomy in 18, leadership, education, or oversight in 14, and safety or human oversight in nine. Among 23 reviews with enumerable, extractable study lists, the CCA was 0.54%, indicating slight overlap. Nursing-specific concerns involved professional judgment, representation, oversight, regulatory variation, and ethical preparedness. Conclusion: AI creates linked governance concerns involving data, fairness, transparency, autonomy, and accountability. Auditable responsibilities are needed across clinical, institutional, and regulatory levels while preserving nursing judgment and patient advocacy. Evidence for specific regulatory models remains limited.
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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.