ArticleBMC nursing2026
Advantages and challenges for utilization of generative artificial intelligence in clinical nursing practice: an integrative review.
Article in BMC nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Development and Effects of a Metaverse-Based Generative AI Nursing Informatics Competency Education Program for Clinical Nurses.Nursing reports (Pavia, Italy) · 2026Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
backgroundGenerative artificial intelligence (AI) has the potential to ease the administrative burden placed on nurses and to advance the quality and efficiency of care. Emerging nursing evidence suggests that increasing clinical complexity is associated with missed care, indicating that generative AI may play a role in supporting clinical judgment and streamlining nursing workflows. Despite these possibilities, its use within nursing area remains at an early developmental stage. This study aims to explore current applications of generative AI in clinical nursing, and to identify its advantages and challenges.
methodsThis integrative review followed the updated methodology of Whittemore and Knafl in 2005. Individual studies in this review were selected based on their relevance to the utilization of generative AI in clinical nursing practice. A literature search was conducted using PubMed, Cochrane, CINAHL, Web of Science, EMBASE, SCOPUS, and Google Scholar, covering the period from January 2000 to September 2025. A quality assessment was performed using a mixed-methods appraisal tool.
resultsThe 15 included studies, which were published between 2023 and 2025, comprised randomized controlled trials, cross-sectional studies, qualitative studies, and mixed-methods studies. In clinical nursing practice, generative AI is mainly utilized in three areas: clinical decision-making support, patient education and self-management support for chronic diseases, and efficiency of nursing work. The most common purpose of generative AI is to enhance nursing efficiency. ChatGPT is most frequently used in clinical decision-making support and enhancing nursing workflows, while task-oriented chatbots are primarily applied to patient education and self-management. Generative AI requires enhanced accuracy and reliability through continuous learning from new data, empathic conversations, and human interaction.
conclusionsOur findings suggest that the use of generative AI in nursing practice has the potential to support clinical decision-making, educate patients and enable self-management, and improve nursing efficiency. By reducing documentation burdens, optimizing workflows and enabling personalized care, generative AI could enhance nursing practice. However, these findings should be interpreted cautiously given the heterogeneity of study designs and the predominantly exploratory nature of the available evidence. The integration of generative AI into nursing practice requires continued improvements in accuracy, reliability, empathic interaction and meaningful human involvement. These potential benefits can be realized by nurses, with appropriate competencies and a critical understanding of both the advantages and limitations of generative AI being fostered. CLINICAL TRIAL NUMBER: Not applicable.
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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.