Generative AI at the Bedside: An Integrative Review of Applications and Implications in Clinical Nursing Practice
Keywords
artificial intelligence, generative AI, nursing informatics, clinical nursing, technology, patient care, nurse-patient relationship, communication, chatbots
Abstract
Aim
The 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.
Design
Integrative review following Whittemore and Knafl's five-stage framework.
Methods
Systematic 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.
Results
Included 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.
Conclusion
Generative 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.
Original Publication Citation
Watson, A. L., Bond, C., Aveyard, H., Smith, G. D., & Jackson, D. (2025). Generative AI at the Bedside: An Integrative Review of Applications and Implications in Clinical Nursing Practice. Journal of Clinical Nursing. https://doi.org/10.1111/jocn.70151
BYU ScholarsArchive Citation
Watson, Adrianna Lorraine PhD, RN, CCRN, TCRN; Bond, Carmel; Aveyard, Helen; Smith, Graeme Drummond; and Jackson, Debra, "Generative AI at the Bedside: An Integrative Review of Applications and Implications in Clinical Nursing Practice" (2025). Faculty Publications. 7920.
https://scholarsarchive.byu.edu/facpub/7920
Document Type
Peer-Reviewed Article
Publication Date
2025-11-26
Publisher
Journal of Clinical Nursing
Language
English
College
Nursing
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