Akademik Veri Yönetim Sistemi
Nurse Education Today, cilt.160, 2026 (SCI-Expanded, SSCI, Scopus)
Background: The increasing integration of artificial intelligence (AI) into healthcare has generated interest in its potential role within nursing practice, particularly in relation to clinical decision-making and care delivery processes. Objectives: This study aimed to examine the relationship between nurses' attitudes toward AI and their clinical decision-making tendencies. Design: This was a cross-sectional and descriptive study. Methods: Data were collected from 323 nurses working in various clinical units of a training and research hospital in Turkey. The Artificial Intelligence Attitude Scale for Nurses and the Nursing Decision-making Instrument were used. Data were analyzed using descriptive statistics, correlation, and regression analyses. Results: The mean score for nurses' attitudes toward AI was 107.19 ± 24.43, indicating a generally positive attitude. For clinical decision-making, the mean score was 67.17 ± 7.12, indicating an analytical decision-making tendency. A strong and significant negative correlation was found between attitudes toward AI and decision-making scores (r = −0.662, p < 0.001). Consistent with the instrument's scoring, these lower scores reflect an analytical decision-making tendency. Simple linear regression analysis demonstrated that attitudes toward AI significantly predicted decision-making scores, explaining 43.8% of the total variance. Additionally, marital status, educational level, professional experience, and clinical unit were significantly associated with both AI attitudes and decision-making tendencies (p < 0.05). Conclusion: The findings indicate that nurses generally report positive attitudes toward AI, and that these attitudes are associated with analytical tendencies in clinical decision-making. AI technologies may function as supportive tools in clinical practice and may be associated with patient safety and care quality. Integrating AI-related competencies into nursing education may support evidence-based and systematic clinical decision-making.