ReviewJournal of clinical nursing2026
Generative AI at the Bedside: An Integrative Review of Applications and Implications in Clinical Nursing Practice.
Review in Journal of clinical nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
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
10 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Application of Large Language Models in Chronic Disease Care: Mixed Methods Systematic Review and Thematic Synthesis.Journal of medical Internet research · 2026Pooled it
- Effectiveness of Artificial Intelligence-Based Nursing Interventions for Chronic Illness Care: Umbrella Review.JMIR nursing · 2026Pooled it
- Generative AI at the Bedside: An Integrative Review of Applications and Implications in Clinical Nursing Practice.Journal of clinical nursing · 2026Review
- The Promise and Peril of Generative AI in Nursing: Moving Beyond Hype Toward Responsible Innovation.Journal of clinical nursing · 2026Article
- AI in Health care: A Catalyst for Enhancement, Not Replacement.Journal of clinical nursing · 2026Article
- How Does Artificial Intelligence Align With Person-Centred Principles in Mental Health Nursing? A Scoping Review.International journal of mental health nursing · 2026Article
- Role reconstruction and competency requirements of emergency nurses in the human-machine collaborative mode: a qualitative study.Frontiers in public health · 2026Article
- Reframing Person-Centered Fundamental Care in the Age of Artificial Intelligence, Robotics and Posthumanization: A Theory-Informed Narrative Review.Journal of multidisciplinary healthcare · 2026Review
- Head Nurse Digital Leadership and Staff Nurse-GenAI Collaboration in Chinese Hospitals: The Mediating Role of Digital Self-Efficacy and the Moderating Role of Nurse Perceived Job Autonomy.Journal of nursing management · 2026Article
- From algorithms to clinical execution: A cross-validated knowledge atlas of AI-enabled precision care (2015-2025).Digital healthArticle
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
aimThe aim of this integrative review is to critically appraise and synthesise empirical evidence on the clinical applications, outcomes, and implications of generative artificial intelligence in nursing practice.
designIntegrative review following Whittemore and Knafl's five-stage framework.
methodsSystematic searches were performed for peer-reviewed articles and book chapters published between 1 January 2018 and 30 June 2025. Two reviewers independently screened titles/abstracts and full texts against predefined inclusion/exclusion criteria focused on generative artificial intelligence tools embedded in nursing clinical workflow (excluding nursing education-only applications). Data were extracted into a standardised matrix and appraised for quality using design-appropriate checklists. Guided by Whittemore and Knafl's integrative review framework, a constant comparative analysis was applied to derive the main themes and subthemes. DATA SOURCES: CINAHL, MEDLINE, and Embase.
resultsIncluded literature was a representative mix of single-group quality improvement pilots, mixed-method usability and feasibility studies, randomised controlled trials, qualitative descriptive and phenomenological studies, as well as preliminary and proof-of-concept observational research. Four overarching themes emerged: (1) Workflow Integration and Efficiency, (2) AI-Augmented Clinical Reasoning, (3) Patient-Facing Communication and Education, and (4) Role Boundaries, Ethics and Trust.
conclusionGenerative artificial intelligence holds promise for enhancing nursing efficiency, supporting clinical decision making, and extending patient communication. However, consistent human validation, ethical boundary setting, and more rigorous, longitudinal outcome and equity evaluations are essential before widespread clinical adoption. IMPLICATIONS FOR THE PROFESSION AND PATIENT CARE: Although generative artificial intelligence could reduce nurses' documentation workload and routine decision-making burden, these gains cannot be assumed. Safe and effective integration will require rigorous nurse training, robust governance, transparent labelling of AI-generated content, and ongoing evaluation of both clinical outcomes and equity impacts. Without these safeguards, generative artificial intelligence risks introducing new errors and undermining patient safety and trust. REPORTING
methodPRISMA 2020.
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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.