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Journals / Journal of Health Sciences and Management / 2026 / Cilt: 6 Sayı: 3

Artificial Intelligence Technologies in Surgical Nursing Care

Cerrahi Hemşirelik Bakımında Yapay Zeka Teknolojileri

İrem Erden(MARMARA ÜNİVERSİTESİ, SAĞLIK BİLİMLERİ ENSTİTÜSÜ)0009-0009-2651-509X
Hamdiye Banu Katran(MARMARA ÜNİVERSİTESİ, SAĞLIK BİLİMLERİ FAKÜLTESİ, HEMŞİRELİK BÖLÜMÜ, CERRAHİ HASTALIKLARI VE HEMŞİRELİĞİ ANABİLİM DALI)0000-0001-5095-6316
Betül Çınar(MARMARA ÜNİVERSİTESİ, SAĞLIK BİLİMLERİ FAKÜLTESİ, HEMŞİRELİK BÖLÜMÜ, HEMŞİRELİK PR.)0009-0000-5172-6336
Aysun Yılmaz(MARMARA ÜNİVERSİTESİ, SAĞLIK BİLİMLERİ FAKÜLTESİ, HEMŞİRELİK BÖLÜMÜ, HEMŞİRELİK PR.)0009-0002-5524-6094
Elif Sude Hayırlı(MARMARA ÜNİVERSİTESİ, SAĞLIK BİLİMLERİ FAKÜLTESİ, HEMŞİRELİK BÖLÜMÜ, HEMŞİRELİK PR.)0009-0000-7871-8440
Pages
113–123
Publication Date
2026-09-30
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

However, it is crucial that these advancements are implemented in a manner that is ethical, safe, and equitable.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:As a part of the technological transformation in healthcare, artificial intelligence (AI) applications are reshaping the quality of surgical nursing care.
📊
Key Findings & Data:This review aims to examine the current state of AI applications in the field of surgical nursing and their impact on nursing care.
💡
Conclusion & Impact:However, it is crucial that these advancements are implemented in a manner that is ethical, safe, and equitable.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:Yapay Zekâ · Cerrahi Hemşireliği · Hemşirelik Bakımı

Abstract

As a part of the technological transformation in healthcare, artificial intelligence (AI) applications are reshaping the quality of surgical nursing care. This review aims to examine the current state of AI applications in the field of surgical nursing and their impact on nursing care. Within the scope of this review, the contributions of AI-based systems to patient monitoring, treatment management, education, communication, and physical support across all stages of perioperative care—from preoperative to postoperative—were evaluated, and the influence of decision-support systems on clinical outcomes was explored. Existing literature indicates that AI has the potential to enhance both patient safety and the quality of care. However, it is crucial that these advancements are implemented in a manner that is ethical, safe, and equitable.

Özet

Sağlık hizmetlerinde teknolojik dönüşümün bir parçası olarak yapay zekâ (YZ) uygulamaları, cerrahi hemşirelik bakımının niteliğini dönüştürmektedir. Bu derlem, YZ uygulamalarının cerrahi hemşireliği alanındaki mevcut durumunu ve hemşirelik bakımına olan etkilerini incelemeyi amaçlamaktadır. Derleme kapsamında YZ tabanlı sistemlerin ameliyat öncesinden sonrasına kadarki tüm cerrahi bakım süreçlerinde hasta izlemi, tedavi yönetimi, eğitim, iletişim ve fiziksel destek gibi alanlardaki katkıları ele alınmış olup karar destek sistemlerinin (KDS) klinik sonuçlara etkisi değerlendirilmiştir. Literatürdeki mevcut çalışmalar, YZ’nin hem hasta güvenliğini hem de bakım kalitesini artırma potansiyeli taşıdığını göstermektedir. Ancak bu gelişmelerin etik ilkelere uygun, güvenli ve eşitlikçi biçimde uygulanması büyük önem taşımaktadır.

🔖

Cite This Article

Erden, İ., Katran, H. B., Çınar, B., Yılmaz, A., Hayırlı, E. S. (2026). Artificial Intelligence Technologies in Surgical Nursing Care. Journal of Health Sciences and Management, Cilt: 6 Sayı: 3, 113–123. https://ajindex.com/articles/1592304

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References (55)

  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, 57(6), 957–966.
  2. Adeyemo, A., Coffey, A., & Kingston, L. (2025). Utilisation of robots in nursing practice: An umbrella review. BMC Nursing, 24(1), 247.
  3. Agencias. (2021, June 10). Conoce a Grace, el primer robot sanitario diseñado para tratar a los pacientes de COVID-19. Público. Erişim adresi: https://www.publico.es/landing/suscripciones?from=cmp
  4. Akalın, B., & Veranyurt, Ü. (2022). Sağlık 4.0 ve sağlıkta yapay zekâ. Sağlık Profesyonelleri Araştırma Dergisi, 4(1), 57–64.
  5. Al-Ahmari, S., & Nadeem, F. (2025). Improving surgical site infection prediction using machine learning: Addressing challenges of highly imbalanced data. Diagnostics, 15(4), 501.
  6. Andrei, M. (2011, March 11). How would you respond to being touched by a robotic nurse? ZME Science. Erişim adresi: https://www.zmescience.com/medicine/mind-and-brain/robot-nurse-11032011/
  7. Aygin, D., & Gül, A. (2021). Geçmişten günümüze cerrahi ve cerrahi hemşireliğinin yeri. İstanbul Gelişim Üniversitesi Sağlık Bilimleri Dergisi, 15, 692–704.
  8. Bihorac, A., Ozrazgat-Baslanti, T., Ebadi, A., Motaei, A., Madkour, M., Pardalos, P. M., Lipori, G., Hogan, W. R., Efron, P. A., Moore, F., Moldawer, L. L., Wang, D. Z., Hobson, C. E., Rashidi, P., Li, X., & Momcilovic, P. (2019). MySurgeryRisk: Development and validation of a machine-learning risk algorithm for major complications and death after surgery. Annals of Surgery, 269(4), 652–662.
  9. Brom, H., Brooks Carthon, J. M., Ikeaba, U., & Chittams, J. (2020). Leveraging electronic health records and machine learning to tailor nursing care for patients at high risk for readmissions. Journal of Nursing Care Quality, 35(1), 27–33.
  10. Çamlı, D. Ç. (2024). Cerrahi hemşireliğinde yapay zekâ teknolojilerinin kullanımı: Etik ikilem. Euroasia Journal of Mathematics, Engineering, Natural & Medical Sciences, 11(34), 26–34.
  11. Çoban, N., Eryiğit, T., Dülcek, S., Beydağ, D. K., & Ortabağ, T. (2022). Hemşirelik mesleğinde yapay zekâ ve robot teknolojilerinin yeri. Fenerbahçe University Journal of Health Sciences, 2(1), 378–385.
  12. Demirdağ, H., & Ergüzel, T. T. (2024). Akıllı cerrahi: Yapay zekanın cerrahi süreçte kullanımı. In H. Uyar Hazar (Ed.), Sağlık & bilim 2024: Hemşirelik-III (1st ed., pp. 37–50). Efe Akademi Yayınları.
  13. Deol, E. S., Henning, G., Basourakos, S., Vasdev, R. M. S., Sharma, V., Kavoussi, N. L., Karnes, R. J., Leibovich, B. C., Boorjian, S. A., & Khanna, A. (2024). Artificial intelligence model for automated surgical instrument detection and counting: An experimental proof-of-concept study. Patient Safety in Surgery, 18(1), 24.
  14. Doğan Merih, Y., & Akdoğan, E. (2021, November 24–26). Hemşirelikte yapay zekâ [Conference paper]. 4th International Eurasian Conference on Biological and Chemical Sciences, Ankara, Türkiye.
  15. El Arab, R. A., Al Moosa, O. A., Sagbakken, M., Ghannam, A., Abuadas, F. H., Somerville, J., & Al Mutair, A. (2025). Integrative review of artificial intelligence applications in nursing: Education, clinical practice, workload management, and professional perceptions. Frontiers in Public Health, 13, 1619378.
  16. Ergenç, M. (2025). Artificial intelligence in surgical practice: Truth beyond fancy covering. Turkish Journal of Surgery, 41(2), 118–120.
  17. Gasmi, A. (2022). Deep learning and health informatics for smart monitoring and diagnosis. arXiv.
  18. Guo, L., Zuo, H., Sun, Y., & Qian, C. (2025). Elevating surgical outcomes: A comprehensive analysis of innovative nursing practices in intraoperative care across diverse specialties. Frontiers in Medicine, 12, 1665160.
  19. Gupta, P. (2016, August 3). Senior citizens can be benefitted with quality robotic assistance from SAM. Engineers Garage. Erişim adresi: https://www.engineersgarage.com/senior-citizens-can-be-benefitted-with-quality-robotic-assistance-from-sam/?utm_source=chatgpt.com
  20. Harris, J., & Matthews, J. (2024). Artificial intelligence: Predicting perioperative problems. British Journal of Hospital Medicine, 85(8), 1–4.
  21. Hassanein, S., El Arab, R. A., Abdrbo, A., Abu-Mahfouz, M. S., Gaballah, M. K. F., Seweid, M. M., Almari, M., & Alzghoul, H. (2025). Artificial intelligence in nursing: An integrative review of clinical and operational impacts. Frontiers in Digital Health, 7, 1552372.
  22. IoT Türkiye. (2021, April 6). Robotik alanındaki gelişmeler sağlık sektörünü nasıl değiştiriyor? Başlangıç Noktası. Erişim adresi: https://baslangicnoktasi.org/robotik-alanindaki-gelismeler-saglik-sektorunu-nasil-degistiriyor
  23. Kahveci, M., & Uğur, L. (2025). Prediction and stage classification of pressure ulcers in intensive care patients by machine learning. Diagnostics, 15(10), 1239.
  24. Kandemir, F., Azizoğlu, F., & Terzi, B. (2023). Hemşirelikte yapay zekâ ve robot teknolojilerinin kullanımı. Yoğun Bakım Hemşireliği Dergisi, 27(2), 118–127.
  25. Ke, Y. H., Jin, L., Elangovan, K., Ong, B. W. X., Oh, C. Y., Sim, J., Loh, K. W.-T., Soh, C. R., Cheng, J. M. H., Lee, A. K. Y., Ting, D. S. W., Liu, N., & Abdullah, H. R. (2024). Real-world deployment and evaluation of PErioperative AI CHatbot (PEACH): A large language model chatbot for perioperative medicine. arXiv.
  26. Lee, J. C., Hamill, C. S., Shnayder, Y., Buczek, E., Kakarala, K., & Bur, A. M. (2024). Exploring the role of artificial intelligence chatbots in preoperative counseling for head and neck cancer surgery. The Laryngoscope, 134(6), 2757–2761.
  27. Lynch, M. (2019, October 9). Kids learn better when playing with robots. RobotLAB. Erişim adresi: https://www.robotlab.com/blog/kids-learn-better-when-playing-with-robots
  28. Maheshwari, K., Cywinski, J. B., Papay, F., Khanna, A. K., & Mathur, P. (2023). Artificial intelligence for perioperative medicine: Perioperative intelligence. Anesthesia & Analgesia, 136(4), 637–645.
  29. Masala, G. L., & Giorgi, I. (2025). Artificial intelligence and assistive robotics in healthcare services: Applications in silver care. International Journal of Environmental Research and Public Health, 22(5), 781.
  30. Navarrete-Welton, A. J., & Hashimoto, D. A. (2020). Current applications of artificial intelligence for intraoperative decision support in surgery. Frontiers of Medicine, 14, 369–381.
  31. Nigar. (2018, April 25). Dünyaca ünlü robot Sophia ilk kez Türkiye’ye geldi. İTÜ ARI Teknokent. Erişim adresi: https://blog.ariteknokent.com.tr/dunyaca-unlu-robot-sophia-ilk-kez-turkiyeye-geldi/
  32. Ölçer, İ., & Yılmaz, A. (2021). Yapay zekanın cerrahi uygulamalara entegrasyonu. Beykent Üniversitesi Fen ve Mühendislik Bilimleri Dergisi, 13(2).
  33. Paracchini, S., Taliento, C., Pellecchia, G., Tius, V., Tavares, M., Borghi, C., Buda, A. A., Bartoli, A., Bourdel, N., & Vizzielli, G. (2025). Artificial intelligence in the operating room: A systematic review of AI models for surgical phase, instruments and anatomical structure identification. Acta Obstetricia et Gynecologica Scandinavica, 104(11), 2054–2064.
  34. Pei, J., Guo, X., Tao, H., Wei, Y., Zhang, H., Ma, Y., & Han, L. (2023). Machine learning-based prediction models for pressure injury: A systematic review and meta-analysis. International Wound Journal, 20(10), 4328–4339.
  35. Rajagopal, A., Ayanian, S., Ryu, A. J., Qian, R., Legler, S. R., Peeler, E. A., Issa, M., Coons, T. J., & Kawamoto, K. (2024). Machine learning operations in health care: A scoping review. Mayo Clinic Proceedings: Digital Health, 2(3), 421–437.
  36. Rony, M. K. K., Das, A., Khalil, M. I., Peu, U. R., Mondal, B., Alam, M. S., Shaleah, A. Z. M., Parvin, M. R., Alrazeeni, D. M., & Akter, F. (2025). The role of artificial intelligence in nursing care: An umbrella review. Nursing Inquiry, 32(2), e70023.
  37. Sarsby, S. (2019, October 28). £34 million investment to develop care robots to transform care sector and relieve pressure off healthcare professionals. AT Today. Erişim adresi: https://attoday.co.uk/34-million-investment-to-develop-care-robots-to-transform-care-sector-and-relieve-pressure-off-healthcare-professionals
  38. Servaty, R., Kersten, A., Brukamp, K., Möhler, R., & Mueller, M. (2020). Implementation of robotic devices in nursing care. Barriers and facilitators: An integrative review. BMJ Open, 10(9), e038650.
  39. Shi, X., Cui, Y., Wang, S., Pan, Y., Wang, B., & Lei, M. (2024). Development and validation of a web-based artificial intelligence prediction model to assess massive intraoperative blood loss for metastatic spinal disease using machine learning techniques. The Spine Journal, 24(1), 146–160.
  40. Sirihorachai, R., Saylor, K. M., & Manojlovich, M. (2022). Interventions for the prevention of retained surgical items: A systematic review. World Journal of Surgery, 46, 370–381.
  41. SoftBank Robotics US. (2016, December 2). Coding a conversation: How Pepper communicates. Medium. Erişim adresi: https://softbankrobotics.medium.com/coding-a-conversation-how-pepper-communicates-e030d0050c1c
  42. Song, Y., Gao, J., Chen, Y., Shi, J., Zhang, X., Luo, D., Wang, M., Wang, Y., Zang, Q., Pei, Z., Bai, Y., & Xu, G. (2025). Nursing robots can reduce nursing workload in general adult wards: A two-phase study. Journal of Nursing Management, 2025, 9096837.
  43. Swiss Medical Network. (2026). Da Vinci. Erişim adresi: https://www.swissmedical.net/en/hospitals/genolier/specialties/robots/da-vinci
  44. Umman, V., Tosun, B., Uygur, A., & Emre, S. (2025). Evaluation of the effectiveness, safety, and patient satisfaction of artificial intelligence-based patient education and counseling for both recipients and donors in the preoperative and postoperative phases of organ transplantation. Transplantation Proceedings, 57(9), 1832–1839.
  45. van der Meijden, S. L., van Boekel, A. M., Schinkelshoek, L. J., van Goor, H., Steyerberg, E. W., Nelissen, R. G. H. H., Mesotten, D., Geerts, B. F., de Boer, M. G. J., Arbous, M. S., PERISCOPE Group, de Heer, P., Hamming, J., Veldkamp, K. E., Peul, W., Lindsetmo, R.-O., Kummeling, M., Beltman, J., Hutteman, M., … Huiskens, J. (2025). Development and validation of artificial intelligence models for early detection of postoperative infections (PERISCOPE): A multicentre study using electronic health record data. The Lancet Regional Health – Europe, 49, 101163.
  46. Vasquez, B., Moreno-Lacalle, R., Soriano, G. P., Juntasoopeepun, P., Locsin, R. C., & Evangelista, L. S. (2023). Technological machines and artificial intelligence in nursing practice. Nursing & Health Sciences, 25(3), 474–481.
  47. Ventura-Silva, J., Martins, M. M., Trindade, L. d. L., Faria, A. d. C. A., Pereira, S., Zuge, S. S., & Ribeiro, O. M. P. L. (2024). Artificial intelligence in the organization of nursing care: A scoping review. Nursing Reports, 14(4), 2733–2745.
  48. Vine, M., Joseph, K., Gibson, D., Lim, B., Chua, M., Siu, A., et al. (2024). Innovative approaches to preoperative care including feasibility, efficacy, and ethical implications: A narrative review. AME Surgical Journal, 4.
  49. Whooley, S. (2020, January 3). How Stäubli and ARxIUM collaborated on pharma automation. Pharmaceutical Processing World. Erişim adresi: https://www.pharmaceuticalprocessingworld.com/how-staubli-and-arxium-collaborated-on-pharma-automation
  50. Wu, G., Cheligeer, C., Southern, D. A., Martin, E. A., Xu, Y., Leal, J., Ellison, J., Bush, K., Williamson, T., Quan, H., & Eastwood, C. A. (2023). Development of machine learning models for the detection of surgical site infections following total hip and knee arthroplasty: A multicenter cohort study. Antimicrobial Resistance & Infection Control, 12, 88.
  51. Xu, P., Liu, M., Liu, M., & Shen, A. (2025). Artificial intelligence in surgical oncology: A comprehensive review from preoperative planning to postoperative care. Intelligent Oncology, 1(4), 267–276.
  52. Yadgarov, M. Y., Landoni, G., Berikashvili, L. B., Polyakov, P. A., Kadantseva, K. K., Smirnova, A. V., Kuznetsov, I. V., Shemetova, M. M., Yakovlev, A. A., & Likhvantsev, V. V. (2024). Early detection of sepsis using machine learning algorithms: A systematic review and network meta-analysis. Frontiers in Medicine, 11, 1491358.
  53. Yang, Z., Cui, X., & Song, Z. (2023). Predicting sepsis onset in ICU using machine learning models: A systematic review and meta-analysis. BMC Infectious Diseases, 23(1), 635.
  54. Yen, P.-Y., Kellye, M., Lopetegui, M., Saha, A., Loversidge, J., Chipps, E. M., Gallagher-Ford, L., & Buck, J. (2018). Nurses’ time allocation and multitasking of nursing activities: A time motion study. AMIA Annual Symposium Proceedings, 2018, 1137–1146.
  55. Yiğit, S., Berşe, S., & Dirgar, E. (2023). Yapay zekâ destekli dil işleme teknolojisi olan ChatGPT’nin sağlık hizmetlerinde kullanımı. Eurasian Journal of Health Technology Assessment, 7(1), 57–65.