Hemşirelik Öğrencilerinin Sağlıkta Yapay Zeka Kullanımına İlişkin Bilgi ve Görüşlerinin Belirlenmesi

Öz

Bu tanımlayıcı araştırma, Nuh Naci Yazgan Üniversitesi Sağlık Bilimleri Fakültesi Hemşirelik Bölümü öğrencilerinin sağlıkta yapay zekâ (YZ) kullanımına ilişkin bilgi, görüş ve farkındalık düzeylerini belirlemek amacıyla Şubat–Nisan 2024 tarihleri arasında 164 öğrenci ile gerçekleştirilmiştir. Bulgular, öğrencilerin YZ’ye ilişkin bilgi düzeylerinin sınırlı (%58,8’inin kısmen ya da hiç bilgisi yok) olmasına rağmen farkındalıklarının ortalamanın üzerinde olduğunu göstermektedir. Katılımcıların çoğunluğu (%80,9) YZ’nin sağlık alanında kullanımını desteklemekte, mesleki becerilerini zenginleştireceğine (%60,7) ve karar destek sistemlerinde sağlık çalışanlarına yardımcı olacağına (%75,6) inanmaktadır. Bununla birlikte öğrenciler, YZ’nin mesleklerin geleceğini tehdit edebileceği (%39,8), hasta okuryazarlığını artırarak sağlık çalışanları için zorluk yaratabileceği (%43,5) ve etik riskler (cinsiyetçilik, ırkçılık) barındırabileceği (%26,3) endişelerini dile getirmiştir. Ayrıca katılımcıların yalnızca %12,2’sinin YZ ile ilgili bilgiyi derslerden edindiği belirlenmiş, bu durum sağlık bilimleri müfredatında yapısal bir eğitim eksikliğine işaret etmiştir. Sonuç olarak, öğrenciler YZ’yi mesleki destek unsuru olarak görseler de bilgi ve etik konulardaki belirsizlikler, YZ eğitiminin teknik, klinik ve etik boyutlarıyla müfredata entegre edilmesi gerekliliğini ortaya koymaktadır.

Anahtar Kelimeler

• Yapay zeka
• hemşirelik öğrencileri
• sağlık
• eğitim.

Etik Beyan

Araştırmanın Etiği 2024/001-05 numaralı karar no ile Nuh Naci Yazgan Üniversitesi Etik Kurul Komisyonundan alınmıştır.

Teşekkür

Bu çalışmanın yürütülmesi sürecinde verdikleri değerli katkı ve desteklerinden dolayı öğrencilerimiz Kübra BÜYÜKKİRAZ, Keziban ERCİYES, Zeynep ÖRDEK ve Feyza ALTUN’a teşekkür ederiz.

Determining Nursing Students' Knowledge and Opinions Regarding the Use of Artificial Intelligence in Healthcare

Öz

This descriptive study was conducted between February and April 2024 with 164 students from the Nursing Department of the Faculty of Health Sciences at Nuh Naci Yazgan University to determine their knowledge, opinions, and awareness levels regarding the use of Artificial Intelligence (AI) in healthcare. The findings revealed that while students’ knowledge of AI was limited (58.8% had little or no knowledge), their awareness levels were above average. Most participants (80.9%) supported the use of AI in healthcare, believing it would enhance their professional skills (60.7%) and assist healthcare workers in decision-support systems (75.6%). However, students also expressed concerns that AI might threaten the future of healthcare professions (39.8%), increase patient health literacy to a level that could pose challenges for healthcare workers (43.5%), and carry ethical risks such as sexism and racism (26.3%). Only 12.2% of the participants reported obtaining AI-related knowledge from their courses, indicating a structural gap in health sciences curricula. In conclusion, although students view AI as a professional support tool, uncertainties in knowledge and ethics highlight the need for AI education to be integrated into curricula with technical, clinical, and ethical dimensions.

Anahtar Kelimeler

• Artificial intelligence
• nursing students
• health
• education.

Etik Beyan

Research ethics approval for this study was obtained from the Nuh Naci Yazgan University Ethics Committee with decision number 2024/001-05.

Teşekkür

We would like to express our gratitude to our students Kübra BÜYÜKKİRAZ, Keziban ERCİYES, Zeynep ÖRDEK, and Feyza ALTUN for their valuable contributions and support during the execution of this study.

Kaynakça

1. Abdelmohsen, S. A., & Al‐Jabri, M. M. (2025). Artificial intelligence applications in healthcare: A systematic review of their impact on nursing practice and patient outcomes. Journal of Nursing Scholarship. https://doi.org/10.1111/jnu.70040
2. Abuzaid, M. M., Elshami, W., & Fadden, S. M. (2022). Integration of artificial intelligence into nursing practice. Health and Technology, 12(6), 1109-1115. https://doi.org/10.1007/s12553-022-00697-0
3. Alenazi, L., & Al-Anazi, S. H. (2025). Understanding artificial intelligence through the eyes of future nurses. Saudi Medical Journal, 46(3), 238-243. https://doi.org/10.15537/smj.2025.46.3.20241069
4. Almagharbeh, W. T., Alharrasi, M., Rony, M. K. K., Kabir, S., Ahmed, S. K., & Alrazeeni, D. M. (2025). Ethical and institutional readiness for artificial intelligence in nursing: An umbrella review. International Nursing Review, 72(4), e70111.
5. Alqaissi, N., & Qtait, M. (2025). Knowledge, attitudes, practices, and barriers regarding the integration of artificial intelligence in nursing and health sciences education: A systematic review. The Application of Artificial Intelligence in Nursing, 11, 1-10. https://doi.org/10.1177/23779608251374185
6. Amiri, H., [diğer yazarlar]. (2024). Medical and nursing students’ knowledge, perceptions, and attitudes toward artificial intelligence: A systematic review. BMC Medical Education, 24(2), 103–114
7. Arbelaez Ossa, L., Lorenzini, G., Milford, S. R., Shaw, D., Elger, B. S., & Rost, M. (2024). Integrating ethics in AI development: A qualitative study. BMC Medical Ethics, 25(1), 1-11. https://doi.org/10.1186/s12910-023-01000-0
8. Aslan, F., & Subaşı, A. (2022). Hemşirelik eğitimi ve hemşirelik süreci perspektifinden yapay zeka teknolojilerine farklı bir bakış. Sağlık Bilimleri Üniversitesi Hemşirelik Dergisi, 4(3), 153-158. https://doi.org/10.48071/sbuhemsirelik.1109187

9. Baruch, Y., & Holtom, B. C. (2008). Survey response rate levels and trends in organizational research. Human Relations, 61(8), 1139-1160.
10. Chustecki, M. (2024). Benefits and risks of AI in health care: Narrative review. Interactive Journal of Medical Research, 13, e53616. https://doi.org/10.2196/53616
11. Clancy, T. R. (2020). Artificial intelligence and nursing: The future is now. The Journal of Nursing Administration, 50(3), 125-127. https://doi.org/10.1097/NNA.0000000000000855
12. Güler, K., & Atasayar, B. Ş. (2025). The relationship between nursing students’ attitudes toward artificial intelligence and their creative personality traits. International Nursing Review, 72(1), e70008. https://doi.org/10.1111/inr.70008
13. Guo, Z., Huang, L., Yan, J., Zhang, H., Jia, X., Li, M., & Li, H. (2025). Artificial intelligence technology in environmental research and health: Development and prospects. Environment International, 203, 1-7. https://doi.org/10.1016/j.envint.2025.109788
14. Kaplan, M., Çakar, F., & Bingöl, H. (2024). Sağlık alanında yapay zeka kullanımı: Derleme. Muş Alparslan Üniversitesi Sağlık Bilimleri Dergisi, 4(3), 75-85.
15. Karslı, N. (2025). Ethical and theological problems related to artificial intelligence. Eskişehir Osmangazi Üniversitesi İlahiyat Fakültesi Dergisi, 12 (Din ve Yapay Zeka), 1-19. https://doi.org/10.51702/esoguifd.1583408
16. Peltonen, L. M., Topaz, M., Ronquillo, C., Pruinelli, L., Sarmiento, R. F., Badger, M. K., Ali, S., Lewis, A., Georgsson, M., Jeon, E., Tayaben, J. L., Kuo, C. H., Islam, T., Sommer, J., Jung, H., Eler, G. J., & Alhuwail, D. (2016). Nursing informatics research priorities for the future: Recommendations from an international survey. Studies in Health Technology and Informatics, 222-226. https://doi.org/10.3233/978-1-61499-658-3-222
17. Sami, A., Tanveer, F., Sajwani, K., Kiran, N., Javed, M. A., Ozsahin, D. U., Muhammad, K., & Waheed, Y. (2025). Medical students' attitudes toward AI in education: perception, effectiveness, and its credibility. BMC medical education, 25(1), 82. https://doi.org/10.1186/s12909-025-06704-y
18. Sarraf, B., & Ghasempour, A. (2025). Impact of artificial intelligence on electronic health record-related burnouts among healthcare professionals: Systematic review. Frontiers in Public Health, 13, 1-13. https://doi.org/10.3389/fpubh.2025.1628831
19. Sengul, T., Bilgic, S., Macit, B., Sevim, F., Alik, S., & Kirkland-Kyhn, H. (2025). Evaluation of nursing students’ ethical decision-making biases and attitudes toward artificial intelligence in nursing education. Nurse Education in Practice, 86, 104432. https://doi.org/10.1016/j.nepr.2025.104432
20. Shishehgar, S., Wilson, V., & Tait, B. (2025). Artificial intelligence in health education and practice: A systematic review of health students’ and academics’ knowledge, perceptions and experiences. BMC Medical Education, 25(1), 87. https://doi.org/10.1186/s12909-025-08701-4
21. Sommer, D., Kasbauer, J., Jakob, D., Schmidt, S., & Wahl, F. (2024). Potential of assistive robots in clinical nursing: An observational study of nurses’ transportation tasks in rural clinics of Bavaria, Germany. Nursing Reports, 14(1), 267-286. https://doi.org/10.3390/nursrep14010021 Stokes, F., & Palmer, A. (2020). Artificial intelligence and robotics in nursing: Ethics of caring as a guide to dividing tasks between AI and humans. Nursing Philosophy, 21(4). https://doi.org/10.1111/nup.12306 Sun, G., & Hopkins, L. (2025). Artificial intelligence and advanced practice nursing. The Nurse Practitioner, 50(7), 34-40. https://doi.org/10.1097/01.NPR.0000000000000334
22. Swinckels, L., Bennis, F. C., Ziesemer, K. A., Scheerman, J. F. M., Bijwaard, H., de Keijzer, A., & Bruers, J. J. (2024). The use of deep learning and machine learning on longitudinal electronic health records for the early detection and prevention of diseases: Scoping review. JMIR Publications, 26. https://doi.org/10.2196/48320
23. Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books. Torreno, F. N., & Torreno, F. N. (2025). Nursing documentation in the AI era: A comparative systematic review and meta-analysis of efficiency, mistakes, stress, and quality of care. Research Square, 1-19. https://doi.org/10.21203/rs.3.rs-7718872/v1
24. Torreno, F. N., & Torreno, F. N. (2025). Nursing documentation in the AI era: A comparative systematic review and meta-analysis of efficiency, mistakes, stress, and quality of care. Research Square, 1-19. https://doi.org/10.21203/rs.3.rs-7718872/v1
25. Van Der Putte, D., Boumans, R., Neerincx, M., Rikkert, M. O., & De Mul, M. (2019). A social robot for autonomous health data acquisition among hospitalized patients: An exploratory field study. International Conference on Human-Robot Interaction, 658-659. https://doi.org/10.1109/HRI.2019.8673280
26. World Economic Forum. (2023). Future of jobs report 2023. Geneva: World Economic Forum. https://www.weforum.org/reports/the-future-of-jobs-report-2023
27. Wu, Y., Xu, M., & Liu, S. (2024). Generative artificial intelligence: A new engine for advancing environmental science and engineering. Environmental Science & Technology, 58, 17524-17528. https://doi.org/10.1021/acs.est.4c07216
28. Yılmaz, D., Uzelli, D., & Dikmen, Y. (2025). Psychometrics of the Attitude Scale towards the use of Artificial Intelligence Technologies in Nursing. BMC Nursing, 24(151), 1-9. https://doi.org/10.1186/s12912-025-02732-7
29. Yılmaz, Y., Uzelli Yılmaz, D., Yıldırım, D., Akın Korhan, E., & Özer Kaya, D. (2021). Yapay zeka ve sağlıkta yapay zekanın kullanımına yönelik sağlık bilimleri fakültesi öğrencilerinin görüşleri. Süleyman Demirel Üniversitesi Sağlık Bilimleri Dergisi, 12(3), 297-308. https://doi.org/10.22312/sdusbed.950372
30. Yükseköğretim Kurulu [YÖK]. (2022). Hemşirelik ulusal çekirdek eğitim programı (HUÇEP-2019). Ankara: Yükseköğretim Kurulu Başkanlığı. https://www.hemed.org.tr/wp-content/uploads/2023/10/hemsirelik_cekirdek_egitim_programi.pdf