Akademik Veri Yönetim Sistemi
Healthcare (Switzerland), cilt.14, sa.7, 2026 (SCI-Expanded, SSCI, Scopus)
Objectives: As artificial intelligence (AI) technologies become increasingly integrated into healthcare systems, understanding healthcare professionals’ psychological responses—particularly AI-related anxiety—has become increasingly important for the safe and effective implementation of these technologies in clinical practice. This study aimed to examine the relationships between oncology nurses’ readiness for artificial intelligence, their attitudes toward artificial intelligence, and their levels of AI-related anxiety. Design: A descriptive, cross-sectional study. Setting: An oncology hospital within a state hospital in Istanbul, Turkey. Participants: A total of 207 oncology nurses working full-time in clinical settings. Methods: Data were collected using an online survey consisting of a demographic information form, the Medical Artificial Intelligence Readiness Scale (MAIRS-MS), the Artificial Intelligence Anxiety Scale (AIAS), and the General Attitudes toward Artificial Intelligence Scale (GAAIS). Spearman correlation analysis, general linear modeling, and conditional mediation analysis were performed using JAMOVI (v2.6.17). A p-value of <0.05 was considered statistically significant. Results: AI-related anxiety was significantly and negatively correlated with both readiness and attitudes toward AI. General linear modeling showed that attitudes toward AI significantly predicted anxiety (β = −0.327, p < 0.001), whereas readiness did not have a direct significant effect. Conditional mediation analysis demonstrated that attitudes fully mediated the relationship between readiness and AI anxiety. The indirect effect of readiness on anxiety through attitudes was stronger among nurses who had received prior AI-related education. While the indirect effect remained significant among untrained nurses, its magnitude was considerably smaller. The total effect of readiness on anxiety was significant only in the untrained group, suggesting that structured education redirects the impact of readiness primarily through attitudes. Conclusions: Attitudes toward artificial intelligence represent the key psychological mechanism linking readiness to AI-related anxiety among oncology nurses. Prior AI education appears to strengthen this relationship by enhancing the association between readiness and attitudes and by being associated with lower anxiety levels. Educational and implementation strategies that emphasize ethical awareness and the development of positive, informed attitudes—rather than focusing solely on technical competence—are likely to be more effective in reducing anxiety and promoting the safe and ethical integration of AI into oncology nursing practice.