Knowledge Of Artificial Intelligence In Nursing Practice: A Meta-analysis Of Perception, Attitude, And Intention

Summary

This meta-analysis examined differences in nurses' perceptions, attitudes, and intentions regarding the use of artificial intelligence (AI) in patient care. Nurses who knew how AI is used in nursing practice had significantly higher perceptions, attitudes, and intentions than those who did not.

Abstract

This meta-analysis aimed to evaluate differences in perception, attitude, and intention regarding AI use in patient care between nurses with and without knowledge of how AI is applied in nursing practice.

We performed a meta-analysis using a continuous outcome model with either fixed- or random-effects methods to estimate the mean difference (MD) and 95% confidence intervals (CIs) for each outcome. We selected 9 studies with 3648 nurses for this meta-analysis. Nurses who know how AI is used in nursing practice had significantly higher perception (pooled raw MD, 1.43; 95% CI, 0.86–1.99, p < 0.001), attitude (MD, 1.80; 95% CI, 0.81–2.78, p < 0.001), and intention (MD, 2.89; 95% CI, 1.61–4.16, p < 0.001) compared to those who do not know how AI is used in nursing practice. However, heterogeneity was very high for all outcomes (I² = 91–98%), indicating substantial variation across studies. However, because these outcomes were measured using instruments with widely varying scale ranges (e.g., 5-point to 100-point scales), the pooled raw MD does not represent a consistent absolute difference. The consistent direction of effect across all studies (positive) is the primary finding, not the specific MD values.

Nurses who know how AI is used in nursing practice report more favorable perceptions, attitudes, and intentions than those who do not. However, due to high heterogeneity, scale variation, and cross-sectional designs, these findings are hypothesis-generating only. Causal claims are not justified.

Introduction

Particularly in the healthcare sector, artificial intelligence has become a crucial disruptive technology. Healthcare workflows, clinical outcomes, and patient care delivery are all being revolutionized by artificial intelligence¹. Nursing aims to provide compassionate, evidence-based care across clinical settings². Nurses must be ready to embrace any technology that enhances overall patient outcomes because they are frontline healthcare professionals. Artificial intelligence is likely to become integrated into nursing practice. To ensure the successful adoption of artificial intelligence in the healthcare sector, it is ....

Protocol

Study design:

This study was a systematic review and meta-analysis conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The completed PRISMA 2020 checklist is provided as Supplementary File 1. The protocol aimed to quantitatively synthesize evidence on nurses' perceptions, attitudes, and intentions regarding the use of Artificial Intelligence (AI) in patient care, comparing those with prior AI knowledge to those without¹⁸. Figure 1 illustrates the study selection process.

Results

After examining 2245 pertinent publications, 9 studies that were published between 2021 and 2025 met the inclusion criteria^{26,27,28,29,30,31,32,33,34}. Table 2 summarizes the findings of these studies. A to.......

Discussion

For the current meta-analysis, 9 studies with 3648 nurses were studied^{26,27,28,29,30,31,32,33,34}. This meta-analysis compared nurses who report knowing how AI is used in nursing practice with those who do not. Across nine .......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Cochrane Library	Cochrane Library	https://www.cochranelibrary.com/
Embase	Embase	https://www.embase.com/landing?status=grey
EndNote X9	Clarivate Analytics	https://support.clarivate.com/Endnote/s/?language=en_US	Reference management software for deduplication and citation organization
Google Scholar	Google	https://scholar.google.com/
Joanna Briggs Institute (JBI) Critical Appraisal Checklist	Joanna Briggs Institute	https://jbi.global/critical-appraisal-tools	Quality assessment tool for analytical cross-sectional studies
Microsoft Excel	Microsoft Corporation	https://www.microsoft.com/en-us	Data extraction form development; data management; conversion of statistics
OVID	OVID	https://www.ovid.com/
PRISMA 2020 Flow Diagram Template	PRISMA Working Group	https://www.prisma-statement.org/prisma-2020-flow-diagram	Template for study selection flow diagram
PubMed	National Institutes of Health	https://pubmed.ncbi.nlm.nih.gov/
Review Manager (RevMan)	The Cochrane Collaboration	Version 5.4	Software for preparing and maintaining Cochrane reviews; used for meta-analysis (pooling, forest plots, heterogeneity, sensitivity analyses).