ArticleBMC nursing2025
Perceived worries in the adoption of artificial intelligence among nurses in neonatal intensive care units.
Article in BMC nursing, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Ethical aspects of artificial intelligence use in neonatal intensive care units: a scoping review.European journal of pediatrics · 2026Article
- A concept analysis of artificial intelligence anxiety among nurses based on Walker and Avant's method.BMC nursing · 2026Article
- Awareness, knowledge and attitudes towards artificial intelligence among nursing students: a systematic review protocol.BMJ open · 2026Article
- Technology use in pediatric cardiovascular surgery intensive care nursing: a qualitative study.BMC nursing · 2026Article
- The effect of artificial intelligence-supported applications on critical thinking motivation and clinical decision making in nursing students: path analysis.BMC medical education · 2026Article
- Artificial intelligence anxiety among nursing and midwifery students: the role of resistance to change - a cross-sectional study.BMC medical education · 2026Article
- Artificial intelligence, robotics, and person-centered care in nursing and midwifery education: qualitative study to develop an augmented caring pedagogy model.BMC medical education · 2026Article
- Medical students perceptions and attitudes toward the use of generative artificial intelligence in clinical decision-making: a nationwide cross-sectional survey in China.BMC medical education · 2026Article
- Health sciences students' attitudes toward artificial intelligence: predictors of ethical awareness, clinical decision-making, and public health perceptions-a cross-sectional study.BMC medical education · 2026Article
- Artificial intelligence self-efficacy and attitudes among nursing students: a multicenter network analysis of educational stratification.BMC medical education · 2026Article
- The Development of Telelearning as an Effort to Improve Nurses' Readiness to Adopt Artificial Intelligence.Journal of patient experience · 2026Article
- Hospital Digital Transformation, Nursing Data Burden, and Leadership and Governance Capacity: A Critical Narrative Review.Journal of nursing management · 2026Review
- Artificial Intelligence in Healthcare: Awareness, Perceptions, and Future Perspectives of Palestinian Medical Students and Physicians.Journal of medical education and curricular developmentArticle
- Nursing Leadership for Digital NICU Transformation: A Systematic Review of Strategies, Digital Health Integration, and Outcomes in High-Risk Neonates.SAGE open nursingReview
- Insights Into Perceived Worries Regarding the Adoption of Artificial Intelligence Among Intensive Care Unit Nurses in the West Bank.SAGE open nursingArticle
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
introductionArtificial Intelligence (AI) comprises computational algorithms designed to analyze data, learn patterns, and execute tasks traditionally requiring human cognition. These models can support public health initiatives, expedite clinical care, and improve diagnosis accuracy. Thus, artificial intelligence in healthcare sectors has the potential to enhance nursing care by assisting nurses with tasks like documentation, workflow improvement, and decision-making, while reducing workforce stress. This study, guided by the Technology Acceptance Model (TAM), assesses perceived worries regarding AI adoption among nurses in neonatal intensive care units (NICUs).
methodsA cross-sectional quantitative design was employed using convenience sampling. Data were collected using the Worries of Applying AI in Healthcare Questionnaire (WAAI-HCQ) from 227 NICU nurses across nine hospitals in the West Bank (January 2-March 3, 2025). SPSS version 26 was used for analysis.
resultsParticipants demonstrated intermediate levels of AI awareness (M = 2.7, SD = 0.5) and limited prior AI experience (M = 2.3, SD = 0.5). Total AI-related worries were moderate (M = 3.2, SD = 0.9), with healthcare provider-related concerns being highest. Multiple linear regression (R² = 0.846) identified education level (B = 0.074, p = 0.026), AI awareness (B = 2.006, p < 0.001), and AI experience (B = -0.959, p < 0.001) as significant predictors, explaining 84.6% of the variance in AI-related worries.
conclusionsNICU nurses in Palestine exhibit moderate AI awareness and concerns, highlighting the need for targeted education and training to address knowledge gaps and facilitate AI integration. This study contributes new knowledge specifically for conflict-affected, resource-constrained NICU settings, where AI implementation faces unique challenges. CLINICAL TRIAL NUMBER: Not applicable.
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