Artificial intelligence literacy and readiness among neonatal nurses: a structural equation modeling study

Abstract

Abstract Background Neonatal Intensive Care Units represent high-risk clinical environments where timely and accurate decision-making is critical for newborn survival, increasing the demand for advanced technological support. As artificial intelligence becomes progressively integrated into neonatal care, it is transforming nurses’ clinical workflows and decision-making processes, underscoring the need to understand their preparedness and perceptions regarding these technologies. Aim This is a cross-sectional and descriptive study to determine the artificial ıntelligence literacy and readiness of neonatal nurses. Methods This was conducted between August 2025 and January 2026, and included 200 neonatal nurses. Data were collected using sociodemographic information, the Artificial Intelligence Literacy Scale (AILS) and the Medical Artificial Intelligence Readiness Scale (MAIRS) and analyzed using structural equation modeling. Results Structural equation modeling supported H1, indicating that AILS significantly and positively predicted MAI-Readiness (B = 0.662, p < .001). AILS explained 57.3% of the variance in MAI-Readiness (R² = 0.573), demonstrating a strong effect. Conclusion This study provides empirical evidence that AI literacy is a significant determinant of AI readiness among neonatal intensive care nurses in Türkiye. The findings indicate that higher levels of AI literacy are associated with greater self-efficacy and willingness to integrate AI technologies into clinical decision-making processes. Healthcare institutions should prioritize structured AI literacy training programs for neonatal intensive care nurses to enhance their self-efficacy and readiness for integrating AI technologies into clinical decision-making processes, thereby ensuring sustainable and ethical AI adoption in high-risk care settings. Clinical trial number Not applicable.