Assessment of Medical Imaging Techniques Students’ Attitudes Towards the Use of Artificial Intelligence in Healthcare and Radiology Practice

Öz

This study was conducted to examine the attitudes of associate degree students in Medical Imaging Techniques (MIT) programs toward artificial intelligence (AI). A total of 159 students enrolled in the MIT program at Istinye University participated. Of the participants, 79.2% were female and 20.8% were male; the mean age was 20.8 ± 2.5 years with a median of 21 (IQR = 20–21). Data were collected using a 21-item, 5-point Likert scale and analyzed with IBM SPSS 25.0. The scale demonstrated high internal consistency (Cronbach’s α = 0.930); the KMO value was 0.910, and Bartlett’s test was significant (χ² = 2371.7, df = 210, p <0.001). Findings indicated that students’ overall attitudes were positive, particularly approving the contribution of AI to professional roles such as image quality assessment (79.9%), patient scheduling (72.3%), and positioning (69.2%). However, a sense of inadequacy in algorithm knowledge and moderate ethical concerns persisted. While no significant gender differences were observed, second-year students scored higher than first-year students in overall attitudes (p = 0.025). Although the single-center design limits the generalizability of the results, this study provides an original and field-specific contribution by being the first to investigate AI-related attitudes among MIT associate degree students. The findings offer guidance for developing educational strategies to integrate AI into the curriculum.

Anahtar Kelimeler

• Artificial intelligence
• Diagnostic Imaging
• Radiology
• Health education

Destekleyen Kurum

Istinye University

Proje Numarası

Bu çalışma, TÜBİTAK Bilim İnsanı Destek Programları Başkanlığı (BİDEB) tarafından yürütülen 2209-A Üniversite Öğrencileri Araştırma Projeleri Destekleme Programı kapsamında desteklenmiştir (2024/1 dönem, Proje No: 1919B012410074).

Etik Beyan

This study was approved by the Istinye University Human Research Ethics Committee (protocol number 2025-122, dated 20.05.2025).

Teşekkür

Prior to the data collection process, participants were informed about the purpose and scope of the research, and participation in the study was ensured by completing an informed consent form online.

Tıbbi Görüntüleme Teknikleri Öğrencilerinin Sağlık Hizmetlerinde ve Radyoloji Pratiğinde Yapay Zeka Kullanımına Yönelik Tutumlarının İncelenmesi

Öz

Bu çalışma, Tıbbi Görüntüleme Teknikleri (TGT) ön lisans öğrencilerinin yapay zekâya (YZ) yönelik tutumlarını incelemek amacıyla gerçekleştirilmiştir. Çalışmaya, İstinye Üniversitesi Sağlık Meslek Yüksekokulu TGT programına kayıtlı 159 öğrenci katılmıştır. Katılımcıların %79,2’si kadın, %20,8’i erkek olup; yaş ortalaması 20,8 ± 2,5, medyanı 21 (IQR=20–21) olarak hesaplanmıştır. Veriler, 21 maddelik 5’li Likert tipi ölçek ile toplanmış ve IBM SPSS 25.0 programı kullanılarak analiz edilmiştir. Ölçek yüksek iç tutarlılık göstermiştir (Cronbach α=0,930); KMO katsayısı 0,910 olup, Bartlett testi anlamlı bulunmuştur (χ²=2371,7, sd=210, p<0,001). Bulgulara göre öğrencilerin genel tutumlarının olumlu olduğu, özellikle yapay zekânın görüntü kalitesi değerlendirme (%79,9), hasta planlama (%72,3) ve pozisyonlandırma (%69,2) gibi mesleki rollere katkısını güçlü biçimde onayladıkları görülmüştür. Bununla birlikte, algoritma bilgisi konusunda yetersizlik hissi ve etik kaygıların belirli düzeyde devam ettiği saptanmıştır. Cinsiyet açısından anlamlı fark bulunmazken, 2. sınıf öğrencilerinin toplam tutum puanları 1. sınıflara göre daha yüksek çıkmıştır (p=0,025). Çalışma, tek merkezli olması nedeniyle genellenebilirliği sınırlı olmakla birlikte, ön lisans düzeyindeki TGT öğrencilerinin YZ’ye yönelik tutumlarının ilk kez incelenmiş olması açısından literatüre özgün ve alana özel katkı sağlamaktadır. Bulgular, YZ’nin müfredata entegrasyonuna yönelik eğitimsel stratejilerin geliştirilmesi için yol gösterici niteliktedir.

Anahtar Kelimeler

• Yapay zeka
• Tanısal görüntüleme
• Radyoloji
• Sağlık eğitimi

Destekleyen Kurum

İstinye Üniversitesi

Proje Numarası

Bu çalışma, TÜBİTAK Bilim İnsanı Destek Programları Başkanlığı (BİDEB) tarafından yürütülen 2209-A Üniversite Öğrencileri Araştırma Projeleri Destekleme Programı kapsamında desteklenmiştir (2024/1 dönem, Proje No: 1919B012410074).

Etik Beyan

Bu çalışma, İstinye Üniversitesi İnsan Araştırmaları Etik Kurulu tarafından onaylanmıştır (20.05.2025 tarihli, 2025-122 protokol numarası).

Teşekkür

Veri toplama süreci öncesinde katılımcılara araştırmanın amacı ve kapsamı açıklanmış, çalışmaya katılım ise bilgilendirilmiş gönüllü onam formunun çevrimiçi olarak onaylanması ile sağlanmıştır.

Kaynakça

1. Alelyani, M., Alamri, S., Alqahtani, M. S., Musa, A., Almater, H., Alqahtani, N., Alshahrani, F., & Alelyani, S. (2021). Radiology community attitude in saudi arabia about the applications of artificial intelligence in radiology. Healthcare (Switzerland), 9(7), 1–10. https://doi.org/10.3390/healthcare9070834
2. Allam, A. H., Eltewacy, N. K., Alabdallat, Y. J., Owais, T. A., Salman, S., Ebada, M. A., Aldare, H. A., Rais, M. A., Salem, M., Al-Dabagh, J. D., Alhassan, M. A., Hanjul, M. M., Mugibel, T. A., Motawea, S. H., Hussein, M., Anas, O. S., Amine, N. M., Almekhlafi, M. A., Mugibel, M. A., … Alkanj, S. (2024). Knowledge, attitude, and perception of Arab medical students towards artificial intelligence in medicine and radiology: A multi-national cross-sectional study. European Radiology, 34(7), 1–14. https://doi.org/10.1007/s00330-023-10509-2
3. Angkurawaranon, S., Inmutto, N., Bannangkoon, K., Wonghan, S., Kham-Ai, T., Khumma, P., Daengpisut, K., Thabarsa, P., & Angkurawaranon, C. (2024). Attitudes and perceptions of Thai medical students regarding artificial intelligence in radiology and medicine. BMC medical education, 24(1), 1188. https://doi.org/10.1186/s12909-024-06150-2
4. Bisdas, S., Topriceanu, C. C., Zakrzewska, Z., Irimia, A. V., Shakallis, L., Subhash, J., Casapu, M. M., Leon-Rojas, J., Pinto dos Santos, D., Andrews, D. M., Zeicu, C., Bouhuwaish, A. M., Lestari, A. N., Abu-Ismail, L., Sadiq, A. S., Khamees, A., Mohammed, K. M. G., Williams, E., Omran, A. I., … Ebrahim, E. H. (2021). Artificial Intelligence in Medicine: A Multinational Multi-Center Survey on the Medical and Dental Students’ Perception. Frontiers in Public Health, 9(December). https://doi.org/10.3389/fpubh.2021.795284
5. Choi, R. Y., Coyner, A. S., Kalpathy-Cramer, J., Chiang, M. F., & Peter Campbell, J. (2020). Introduction to machine learning, neural networks, and deep learning. Translational Vision Science and Technology, 9(2), 1–12. https://doi.org/10.1167/tvst.9.2.14
6. Civaner, M. M., Uncu, Y., Bulut, F., Chalil, E. G., & Tatli, A. (2022). Artificial intelligence in medical education: a cross-sectional needs assessment. BMC Medical Education, 22(1), 1–9. https://doi.org/10.1186/s12909-022-03852-3
7. Coakley, S., Young, R., Moore, N., England, A., O’Mahony, A., O’Connor, O. J., Maher, M., & McEntee, M. F. (2022). Radiographers’ knowledge, attitudes and expectations of artificial intelligence in medical imaging. Radiography, 28(4), 943–948. https://doi.org/10.1016/j.radi.2022.06.020
8. Damerji, H., & Salimi, A. (2021). Mediating effect of use perceptions on technology readiness and adoption of artificial intelligence in accounting. Accountinh Education, 30(2), 107–130. https://doi.org/10.1080/09639284.2021.1872035

9. Doherty, G., McLaughlin, L., Hughes, C., McConnell, J., Bond, R., & McFadden, S. (2024a). A scoping review of educational programmes on artificial intelligence (AI) available to medical imaging staff. Radiography, 30(2), 474–482. https://doi.org/10.1016/j.radi.2023.12.019
10. Doherty, G., McLaughlin, L., Hughes, C., McConnell, J., Bond, R., & McFadden, S. (2024b). Radiographer Education and Learning in Artificial Intelligence (REAL-AI): A survey of radiographers, radiologists, and students’ knowledge of and attitude to education on AI. Radiography, 30, 79–87. https://doi.org/10.1016/j.radi.2024.10.010
11. Eltawil, F. A., Atalla, M., Boulos, E., Amirabadi, A., & Tyrrell, P. N. (2023). Analyzing Barriers and Enablers for the Acceptance of Artificial Intelligence Innovations into Radiology Practice : A Scoping Review. Tomography, 9, 1443–1455.
12. Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593. https://doi.org/10.1111/bjet.12864
13. Habib, M. M., Hoodbhoy, Z., & Siddiqui, M. A. R. (2024). Knowledge, attitudes, and perceptions of healthcare students and professionals on the use of artificial intelligence in healthcare in Pakistan. PLOS Digital Health, 3(5), 1–14. https://doi.org/10.1371/journal.pdig.0000443
14. Jha, N., Shankar, P. R., Al-Betar, M. A., Mukhia, R., Hada, K., & Palaian, S. (2022). Undergraduate Medical Students’ and Interns’ Knowledge and Perception of Artificial Intelligence in Medicine. Advances in Medical Education and Practice, 13(August), 927–937. https://doi.org/10.2147/AMEP.S368519
15. Koç, E. M., Çifçi, İ., Tengiz, F. İ., & Koç, A. M. (2025). Tıp Fakültesi Öğrencilerinin Tıpta Yapay Zekâ ile İlgili Bilgi, Düşünce ve Yaklaşımları. Sağlık Bilimlerinde Yapay Zeka Dergisi, 5(1), 1–6. https://izlik.org/JA77YB76FD
16. Kocak, B., Baessler, B., Cuocolo, R., Mercaldo, N., & Pinto, D. (2023). Trends and statistics of artificial intelligence and radiomics research in Radiology , Nuclear Medicine , and Medical Imaging : bibliometric analysis. European Radiology, 33(11), 7542–7555. https://doi.org/10.1007/s00330-023-09772-0
17. Kwak, Y., & Ahn, J. (2022). Influence of AI ethics awareness , attitude , anxiety , and self-efficacy on nursing students ’ behavioral intentions. BMC Nursing, 21(267).
18. Labrague, L. J., Aguilar-rosales, R., Yboa, B. C., Sabio, J. B., & Santos, A. D. L. (2023). Nurse Education in Practice Student nurses ’ attitudes , perceived utilization , and intention to adopt artificial intelligence (AI) technology in nursing practice : A cross-sectional study. Nurse Education in Practice, 73. https://doi.org/10.1016/j.nepr.2023.103815
19. Li, X., Jiang, M. Y. chao, Jong, M. S. yung, Zhang, X., & Chai, C. S. (2022). Understanding Medical Students’ Perceptions of and Behavioral Intentions toward Learning Artificial Intelligence: A Survey Study. International Journal of Environmental Research and Public Health, 19(14). https://doi.org/10.3390/ijerph19148733
20. Loh, H. W., Ooi, C. P., Seoni, S., Barua, P. D., Molinari, F., & Acharya, U. R. (2022). Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011–2022). Computer Methods and Programs in Biomedicine, 226, 107161. https://doi.org/10.1016/j.cmpb.2022.107161
21. Mehta, N., Harish, V., Bilimoria, K., Morgado, F., Ginsburg, S., Law, M., & Das, S. (2021). Knowledge of and Attitudes on Artificial Intelligence in Healthcare: A Provincial Survey Study of Medical Students. MedEdPublish. https://doi.org/10.15694/mep.2021.000075.1
22. Miró Catalina, Q., Femenia, J., Fuster-Casanovas, A., Marin-Gomez, F. X., Escalé-Besa, A., Solé-Casals, J., & Vidal-Alaball, J. (2023). Knowledge and Perception of the Use of AI and its Implementation in the Field of Radiology: Cross-Sectional Study. Journal of medical Internet research, 25, e50728. https://doi.org/10.2196/50728
23. Muradi Saliha. (2025). Hemşirelik Öğrencilerinin E-Sağlık Okuryazarlıkları ile Yapay Zekaya Yönelik Tutumları. Selçuk Üniversitesi.
24. Najjar, R. (2023). Redefining Radiology : A Review of Artificial Intelligence Integration in Medical Imaging. Diagnostics, 13(2760).
25. Pedersen, M. R. V., Kusk, M. W., Lysdahlgaard, S., Mork-Knudsen, H., Malamateniou, C., & Jensen, J. (2024). Nordic radiographers’ and students’ perspectives on artificial intelligence – A cross-sectional online survey. Radiography, 30(3), 776–783. https://doi.org/10.1016/j.radi.2024.02.020
26. Pinto dos Santos, D., Giese, D., Brodehl, S., Chon, S. H., Staab, W., Kleinert, R., Maintz, D., & Baeßler, B. (2019). Medical students’ attitude towards artificial intelligence: a multicentre survey. European Radiology, 29(4), 1640–1646. https://doi.org/10.1007/s00330-018-5601-1
27. Schepman, A., Rodway, P., & Rodway, P. (2023). The General Attitudes towards Artificial Intelligence Scale ( GAAIS ): Confirmatory Validation and Associations with Personality , Corporate Distrust , and General Trust The General Attitudes towards Artificial Intelligence Scale ( GAAIS ): Confirmatory G. International Journal of Human–Computer Interaction, 39(13), 2724–2741. https://doi.org/10.1080/10447318.2022.2085400
28. Sit, C., Srinivasan, R., Amlani, A., Muthuswamy, K., Azam, A., Monzon, L., & Poon, D. S. (2020). Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey. Insights into Imaging, 11(14), 7–12. https://doi.org/10.1186/s13244-019-0830-7
29. Stewart, J., Lu, J., Gahungu, N., Goudie, A., Fegan, P. G., Bennamoun, M., Sprivulis, P., & Dwivedi, G. (2023). Western Australian medical students’ attitudes towards artificial intelligence in healthcare. PLoS ONE, 18(8 August), 1–14. https://doi.org/10.1371/journal.pone.0290642
30. Teo, T., & Zhou, M. (2014). Explaining the intention to use technology among university students : a structural equation modeling approach. J Comput High Educ, 26, 124–142. https://doi.org/10.1007/s12528-014-9080-3
31. Truong, N. M., Vo, T. Q., Tran, H. T. B., Nguyen, H. T., & Pham, V. N. H. (2023). Healthcare students’ knowledge, attitudes, and perspectives toward artificial intelligence in the southern Vietnam. Heliyon, 9(12), e22653. https://doi.org/10.1016/j.heliyon.2023.e22653
32. Yazıcı, H. G., Ökten, Ç., & Akdulum, G. H. (2025). Hemşirelik Öğrencilerinin Yapay Zekaya Yönelik Genel Tutumları ve Etkileyen Faktörler. Sağlık Bilimleri Dergisi, 34(2), 193–198. https://doi.org/10.34108/eujhs.1541870