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- MakaleYayıncıda açık erişim2026Investigation of the Relationship Between Artificial Intelligence Readiness and Artificial Intelligence Anxiety Levels in Nursing Students; [Hemşirelik Öğrencilerinde Yapay Zeka Hazırbulunuşluk ve Yapay Zeka Kaygı Düzeyleri Arasındaki İlişkinin İncelenmesi]
Yalçın, Suna Uysal; Solhan, Resul
Background: Artificial intelligence and robot technologies in the field of health and nursing practices are a new concept and it is known to have a high potential. For this reason, it is important for nursing students to be prepared for artificial intelligence and not to have problems in their education. In our study, it was aimed to examine the relationship between artificial intelligence readiness and artificial intelligence anxiety levels in nursing students. Methods: This study was descriptive-correlational in nature and was carried out with 176 students between May-June 2024 with a face-to-face questionnaire. “Demographic data Collection Form”, “Artificial Intelligence (AI) Anxiety Scale” and “Medical Artificial Intelligence (MAI) Readiness Scale” were used in the study. Descriptive statistical methods (frequency, mean, percentage analyses), Pearson correlation and linear regression analyses were used in data analysis. Results: 69.3% of the participants were female and 30.7% were male, and the AI anxiety score of female students (45.336 ± 13.747) was higher than that of male students (38.019 ± 16.203). This difference was statistically significant (t = 3.079, p = .002). According to the regression analysis results, medical AI readiness significantly and negatively affects AI anxiety (β = -.150, p .05). This finding indicates that asMAI readiness increases, AI anxiety decreases. Conclusion: In our study, artificial intelligence anxiety and artificial intelligence readiness levels of nursing students were found to be moderate. Nursing undergraduate education curriculum should be updated and courses such as technology/ artificial intelligence in health should be added to the curriculum; students should be informed about the use, positive aspects and advantages of artificial intelligence and their concerns about this issue should be eliminated.
- BildiriYalnız künye2025Brain Tissue Classification from Hyperspectral Images Using Convolutional Neural Networks
Karaduman, Ismet; Pehlivanoglu, Meltem Kurt; Eker, Ayse Gul; Duru, Nevcihan; Dundar, Tolga Turan; Serdar Mutluer, Ahmet; Adali, E.
Many of the decisions made and taken in the field of health are important for human life to be sufficient. In such an important area where human transactions are concerned, artificial intelligence-supported studies have a very important place, especially in recent years. This is the correct classification of four classes (Normal Tissue, Tumor Tissue, Hypervascularized Tissue and Background) separately with the 3D-CNN based deep learning model on the In-vivo Hyperspectral Human Brain Image dataset. For this purpose, firstly, three different data settings with 128 spectral bands were obtained from raw images, each containing 826 spectral bands, with dimension reduction methods. In this way, each data setting was made with the designs used in the 3D-CNN based deep learning model. As a result of the experimental studies, the highest F1 score of 99.69% was achieved with the 3D-CNN-based deep learning model.
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