Perceived worries in the adoption of artificial intelligence among nurses in neonatal intensive care units - Ahmad Ayed; Ahmad Batran; Ibrahim Aqtam; Malakeh Z. Malak; Moath Abu Ejheisheh; Mosaab Farajallah; Lamees Farraj; Sanaa Alkhatib (BMC Nursing)
Abstract Introduction Artificial 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). Methods A 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. Results Participants 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. Conclusions NICU 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.