Ameliyathane Hemşireliğinde Yapay Zekâ Uygulamaları: Mevcut Durum ve Gelecek Perspektifleri

Year 2026, Volume: 2 Issue: 1, 25 - 33, 29.01.2026

Seda Öztürk , Rabia Görücü

Abstract

Bu derleme, ameliyathane hemşireliğinde yapay zekâ teknolojilerinin mevcut kullanım alanlarını incelemeyi, bu teknolojilerin mesleki uygulamalara olan etkilerini değerlendirmeyi ve gelecekteki potansiyel gelişmeleri tartışmayı amaçlamaktadır. Yapay zekâ uygulamalarının etkisi; ameliyat öncesi hazırlık ve hasta değerlendirme, intraoperatif destek ve komplikasyon öngörüsü ile ameliyat sonrası bakım ve hasta izlemi olmak üzere üç temel başlık altında incelenmiştir. Yapay zeka teknolojileri, ameliyat öncesi hazırlıkların kişiselleştirilmesi, cerrahi süreçlerin gerçek zamanlı takibi ve ameliyat sonrası bakım kalitesinin artırılması konularında önemli katkılar sağlamaktadır. Makine öğrenimi algoritmaları sayesinde potansiyel komplikasyonlar erken tespit edilebilmekte; robotik sistemler ve artırılmış gerçeklik teknolojileri ise hemşirelerin cerrahlara daha etkin destek vermesine olanak tanımaktadır. Ancak, bu teknolojilerin entegrasyonu şeffaflık eksikliği, algoritmik önyargılar ve veri güvenliği gibi etik ve teknik sorunları da beraberinde getirmektedir. Ameliyathane hemşireliğinde Yapay zekanın etkili kullanımı için etik, eğitsel ve sistemsel düzenlemelerin yapılması gereklidir. Hemşirelik eğitiminin Yapay zeka okuryazarlığı ile güçlendirilmesi ve hemşirelerin Yapay zeka sistemlerinin geliştirme süreçlerine aktif katılımı, bu teknolojilerin güvenli ve etkin kullanımını sağlayacaktır.

Keywords

Yapay zekâ , ameliyathane hemşireliği , makine öğrenimi , hasta güvenliği , etik

Ethical Statement

Bu çalışma derleme makalesi olduğu için Etik kurul alınmamıştır.

Supporting Institution

Bu araştırma herhangi bir finansman kuruluşundan/sektörden destek almamıştır.

Thanks

Yoktur.

Artificial Intelligence Applications in Operating Room Nursing: Current Practices and Future Perspectives

Abstract

Düz Bağlayıcı 20, ŞekilThis review aims to examine the current applications of artificial intelligence (AI) in operating room nursing, evaluate its impact on professional practices, and discuss potential future developments. The effects are analyzed in three main areas: preoperative preparation and patient assessment, intraoperative support and complication prediction, and postoperative care and patient monitoring. AI technologies significantly contribute to the personalization of preoperative preparations, real-time monitoring of surgical processes, and improvement of postoperative care quality. Machine learning algorithms facilitate early detection of potential complications, while robotic systems and augmented reality technologies enable nurses to better support surgeons. However, AI integration also raises ethical and technical challenges such as lack of transparency, algorithmic bias, and data security. Effective use of AI in operating room nursing requires ethical, educational, and systemic frameworks. Strengthening nursing education with AI literacy and involving nurses in AI development processes are crucial for the safe and effective use of these technologies.

Keywords

Artificial intelligence , Operating room nursing , Machine learning , Patient safety , Ethics.

Ethical Statement

This study is a review article, so no ethics committee approval was required.

Supporting Institution

This research did not receive support from any funding agency/industry.

Thanks

None.

References

• Ahn, J., & Park, H. O. (2023). Development of a case-based nursing education program using generative artificial intelligence. Journal of Korean Academic Society of Nursing Education, 29(3), 234–246.
• Amann, J., Blasimme, A., Vayena, E., Frey, D., & Madai, V. I. (2020). Explainability for artificial intelligence in healthcare: A multidisciplinary perspective. BMC Medical Informatics and Decision Making, 20(1), 310.
• Birkhoff, D. C., van Dalen, A. S. H., & Schijven, M. P. (2021). A review on the current applications of artificial intelligence in the operating room. Surgical Innovation, 28(5), 611–619.
• Cant, R. P., & Cooper, S. J. (2014). Simulation in the Internet age: the place of web-based simulation in nursing education. An integrative review. Nurse Education Today, 34(12), 1435–1442.
• Chen, F., Cui, X., Han, B., Liu, J., Zhang, X., & Liao, H. (2021). Augmented reality navigation for minimally invasive knee surgery using enhanced arthroscopy. Computer Methods and Programs in Biomedicine, 201, 105952.
• Çamlı, D. Ç. (2024). Cerrahi hemşireliğinde yapay zekâ teknolojilerinin kullanımı: Etik ikilem. Euroasia Journal of Mathematics, Engineering, Natural & Medical Sciences, 11(34), 26–34.
• Darwish, S. S., EL Berry, K. I., Elfiky, E. R., Ali, W. A., & Abd El Mageed, H. H. (2023). Artificial Intelligence Robotics Utilization in Relation to Wellbeing, Burnout and Stress among Operating Room Nurses. Menoufia Nursing Journal, 8(3), 107-121.
• Ding, A.-C., Shi, L., Yang, H., & Choi, I. (2024). Enhancing teacher AI literacy and integration through different types of cases in teacher professional development. Computers in Education, 266, 107947.
• Durukan, N., Görücü, R., & Ayoğlu, T. (2025). Knowledge and Opinions of Operating Room Nurses About Artificial Intelligence: A Descriptive Cross‐Sectional Study. Journal of Clinical Nursing, 0, 1-11.
• Ergin, E., Karaarslan, D., Şahan, S., & Bingöl, Ü. (2023). Can artificial intelligence and robotic nurses replace operating room nurses? The quasi-experimental research. Journal of Robotic Surgery, 17(4), 1847–1855.
• Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2019). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118.
• Eyiler, E., Yangın, H., & Boz, İ. (2025). “Hemşirelik bakımında teknolojik yeterlik kuramı”: Bir orta düzey kuram. Akdeniz Hemşirelik Dergisi, 3(3), 118–126. Foronda CL, Fernandez-Burgos M, Nadeau C, Kelley CN, Henry MN. (2020). Virtual Simulation in Nursing Education: A Systematic Review Spanning 1996 to 2018. Simul Healthc. 15(1), 46-54.
• Fu, L., Wu, X., Lou, X., Zhang, Q., & Qiu, D. (2025). Intelligent Analgesia Management System in Postoperative Pain Management: A Retrospective Analysis. Journal of PeriAnesthesia Nursing.
• Geneedy, E. M. G., Hemaida, W. E. M., & Aboelfetoh, E. E. (2024). Implementation of an educational program for operating room nurses to improve perception and attitudes towards integrating artificial intelligence in nursing practice. Egyptian Journal of Health Care, 15(2), 1854–1875.
• Gianfrancesco, M. A., Tamang, S., Yazdany, J., & Schmajuk, G. (2018). Potential biases in machine learning algorithms using electronic health record data. JAMA Internal Medicine, 178(11), 1544–1547.
• Gümüş, E., & Kasap, E. U. (2021). The future of the nursing: Robot nurses. Journal of Artificial Intelligence in Health Sciences, 1(2), 20–25. Harris, J., & Matthews, J. (2024). Artificial intelligence: predicting perioperative problems. British Journal of Hospital Medicine, 85(8), 1-4.
• Huang, P., Yang, J., Zhao, D., Ran, T., Luo, Y., Yang, D., ... & Chen, C. (2025). Machine Learning–Based Prediction of Early Complications Following Surgery for Intestinal Obstruction: Multicenter Retrospective Study. Journal of Medical Internet Research, 27, e68354.
• Irani, C. S., & Chu, C. H. (2022). Evolving with technology: Machine learning as an opportunity for operating room nurses to improve surgical care-A commentary. Journal of Nursing Management, 30(8), 3802–3805.
• Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present and future. Seminars in Cancer Biology, 12, 1–11.
• Kahraman, H., Akutay, S., Yüceler Kaçmaz, H., & Taşci, S. (2025). Artificial intelligence literacy levels of perioperative nurses: The case of Türkiye. Nursing & Health Sciences, 27(1), e70059.
• Karaman, O. (Ed.). (2024). Geleceğin sağlık sistemi: Yapay zekanın rolü ve ileri uygulamalar II. Efe Akademi Yayınları.
• King, C. R., Shambe, A., & Abraham, J. (2023). Potential uses of AI for perioperative nursing handoffs: A qualitative study. JAMIA Open, 6(1), ooad015.
• Li, Y. Y., Wang, J. J., Huang, S. H., Kuo, C. L., Chen, J. Y., Liu, C. F., & Chu, C. C. (2022). Implementation of a machine learning application in preoperative risk assessment for hip repair surgery. BMC Anesthesiology, 22(1), 116.
• Liu, D., Li, X., Nie, X., Hu, Q., Wang, J., Hai, L., ... & Guo, P. (2023). Artificial intelligent patient-controlled intravenous analgesia improves the outcomes of older patients with laparoscopic radical resection for colorectal cancer. European Geriatric Medicine, 14(6), 1403-1410.
• Morley, J., Machado, C. C. V., Burr, C., Cowls, J., Joshi, I., Taddeo, M., & Floridi, L. (2020). The ethics of AI in health care: A mapping review. Social Science & Medicine, 260, 113172.
• Nijkamp, N., & Wakefield, E. (Eds.). (2024). The future of artificial intelligence in perioperative nursing. Journal of Perioperative Nursing, 37(4), e1-e4.
• Öztepe Yeşilyurt, K. (2024). The new dimension of surgery: Telesurgery and surgical nursing. SDU Journal of Health Science Institute/SDÜ Saglik Bilimleri Enstitüsü Dergisi, 15(2), 300. Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine learning in medicine. The New England Journal of Medicine, 380(14), 1347–1358. Russell, S., & Norvig, P. (2020). Artificial intelligence: A modern approach (4th ed.). Pearson.
• Saleh, Z. T., Rababa, M., Elshatarat, R. A., Alharbi, M., Alhumaidi, B. N., Al-Za’areer, M. S., ... & Fadila, D. E. S. (2025). Exploring faculty perceptions and concerns regarding artificial intelligence Chatbots in nursing education: potential benefits and limitations. BMC Nursing, 24(1), 440.
• Schouten, A. M., Butler, R. M., Vrins, C. E., Flipse, S. M., Jansen, F. W., van der Eijk, A. C., & van den Dobbelsteen, J. J. (2025). Impact of operating room technology on intraoperative nurses' workload and job satisfaction: An observational study. International Journal of Nursing Studies Advances, 100341.
• Shenoy, V. N., Foster, E., Aalami, L., Majeed, B., & Aalami, O. (2018, December). Deepwound: Automated postoperative wound assessment and surgical site surveillance through convolutional neural networks. In 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 1017-1021). IEEE.
• Stenseth, H. V., Steindal, S. A., Solberg, M. T., Ølnes, M. A., Sørensen, A. L., Strandell-Laine, C., Olaussen, C., Aure, C. F., Pedersen, I., Zlamal, J., Martini, J. G., Bresolin, P., Linnerud, S. C. W., Nes, A. A. G., et al. (2025). Simulationbased learning supported by technology to enhance critical thinking in nursing students: Scoping review. JMIR Research Protocols, 27, e36725.
• Stokes, F., & Palmer, A. (2020). Artificial intelligence and robotics in nursing: Ethics of caring as a guide to dividing tasks between AI and humans. Journal of Nursing Philosophy, 21(4), e12306.
• Subramanian, A., Cao, R., Naeini, E. K., Aqajari, S. A. H., Hughes, T. D., Calderon, M. D., ... & Rahmani, A. M. (2025). Multimodal pain recognition in postoperative patients: Machine learning approach. JMIR Formative Research, 9, e67969.
• Topaz, M., & Pruinelli, L. (2017). Big data and nursing: Implications for the future. Studies in Health Technology and Informatics, 232, 165–171.
• Topol, E. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.
• Wang, D., Guo, Y., Yin, Q., Cao, H., Chen, X., Qian, H., ... & Zhang, J. (2023). Analgesia quality index improves the quality of postoperative pain management: a retrospective observational study of 14,747 patients between 2014 and 2021. BMC Anesthesiology, 23(1), 281.
• Wagner, L., Jourdan, S., Mayer, L., Müller, C., Bernhard, L., Kolb, S., ... & Wilhelm, D. (2024). Robotic scrub nurse to anticipate surgical instruments based on real-time laparoscopic video analysis. Communications Medicine, 4(1), 156.
• Xu, X., Miao, M., Shi, G., Zhang, P., Liu, P., Zhao, B., & Jiang, L. (2024). Operative positioning and IntraoperativeAcquired pressure injury: A retrospective cohort study. Advances in Skin & Wound Care, 37(3), 148-154.
• Yılmaz, A., & Ölçer, İ. (2021). Yapay zekanın cerrahi uygulamalara entegrasyonu. Beykent Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 13(2), 21–27.
• Zeng, S., Li, L., Hu, Y., Luo, L., & Fang, Y. (2021). Machine learning approaches for the prediction of postoperative complication risk in liver resection patients. BMC Medical Informatics and Decision Making, 21(1), 371.
• Zhou, L., Wang, F., & Wang, L. (2021). Intelligent systems in healthcare: A review of applications. Journal of Healthcare Engineering, 2021, 1–9.
• Zhou, C. M., Li, H., Xue, Q., Yang, J. J., & Zhu, Y. (2024). Artificial intelligence algorithms for predicting postoperative ileus after laparoscopic surgery. Heliyon, 10(5).