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Artificial Intelligence Applications in the Neonatal Intensive Care Unit and Their Evaluation from a Nursing Perspective

Yıl 2026, Cilt: 5 Sayı: 1, 49 - 56, 24.03.2026
https://doi.org/10.61830/balkansbd.1844500
https://izlik.org/JA53FW84MD

Öz

Advances in artificial intelligence technologies have the potential to enhance the quality of care delivered in neonatal intensive care units (NICUs) and have led to significant transformations in nursing practice. The aim of this review is to examine artificial intelligence–based applications used in NICUs in light of the current literature and to evaluate their effects on nursing care. Within the scope of the review, the use of artificial intelligence in areas such as continuous monitoring and surveillance, clinical decision support systems, early detection of life-threatening conditions including apnea and sepsis, assessment of neonatal pain and comfort, prediction of length of hospital stay, and management of nursing workload is discussed. The reviewed studies indicate that artificial intelligence–supported systems strengthen early warning mechanisms by analyzing large and complex datasets, reduce false alarm rates, and improve patient safety. However, the majority of existing studies remain at the prototype or model development stage, and integration into routine clinical practice is still limited. In addition, the integration of artificial intelligence into nursing care raises important concerns related to ethical responsibility, professional autonomy, data privacy, and the preservation of human-centered care. In conclusion, artificial intelligence represents a powerful tool to support nursing care in neonatal intensive care units; however, strengthening education, institutional infrastructure, and ethical guidelines is essential to ensure its safe and effective implementation.

Etik Beyan

This study does not require ethics committee approval as it is a literature review.

Destekleyen Kurum

No funding was received from any institution for this study.

Kaynakça

  • Kaynakça 1. Alıcılar, H. E., & Çöl, M. (2021). Halk sağlığında yapay zekânın kullanımı [The use of artificial intelligence in public health]. Uludağ Üniversitesi Tıp Fakültesi Dergisi, 47(1), 151–158. https://doi.org/10.32708/uutfd.891274
  • 2. Al Khatib, I., & Ndiaye, M. (2025). Examining the role of AI in changing the role of nurses in patient care: A systematic review. JMIR Nursing, 8, e63335. https://doi.org/10.2196/63335
  • 3. American Nurses Association. (2025). What is AI and how is it applied in nursing practice? Retrieved September 25, 2025, from https://www.nursingworld.org/globalassets/practiceandpolicy/nursing-excellence/ana-position-statements/the-ethical-use-of-artificial-intelligence-in-nursing-practice_bod-approved
  • 4. Ayed, A., Batran, A., Aqtam, I., Malak, M. Z., Ejheisheh, M. A., Farajallah, M., Farraj, L., & Alkhatib, S. (2025). Perceived worries in the adoption of artificial intelligence among nurses in neonatal intensive care units. BMC Nursing, 24(1), 777. https://doi.org/10.1186/s12912-025-03318-z
  • 5. Beam, K., Sharma, P., Levy, P., & Beam, A. L. (2024). Artificial intelligence in the neonatal intensive care unit: The time is now. Journal of Perinatology, 44(1), 131–135. https://doi.org/10.1038/s41372-023-01719-z
  • 6. Bodur, G., Çakır, H., Turan, S., Seren, A. K. H., & Göktaş, P. (2025). Artificial intelligence in nursing practice: A qualitative study of nurses’ perspectives on opportunities, challenges, and ethical implications. BMC Nursing, 24(1), 1263. https://doi.org/10.1186/s12912-025-03775-6
  • 7. Carlini, L. P., Coutrin, G. D. A. S., Ferreira, L. A., Soares, J. D. C. A., Silva, G. V. T., Heiderich, T. M., Balda, R. D. C. X., Barros, M. C. D. M., Guinsburg, R., & Thomaz, C. E. (2024). Human vs machine towards neonatal pain assessment: A comprehensive analysis of the facial features extracted by health professionals, parents, and convolutional neural networks. Artificial Intelligence in Medicine, 147, 102724. https://doi.org/10.1016/j.artmed.2023.102724
  • 8. Chen, B., Alrifai, W., Gao, C., Jones, B., Novak, L., Lorenzi, N., France, D., Malin, B., & Chen, Y. (2021). Mining tasks and task characteristics from electronic health record audit logs with unsupervised machine learning. Journal of the American Medical Informatics Association, 28(6), 1168–1177. https://doi.org/10.1093/jamia/ocaa338
  • 9. Chen, X., Zhu, H., Mei, L., Shu, Q., Cheng, X., Luo, F., Zhao, Y., Chen, S., & Pan, Y. (2023). Video-based versus on-site neonatal pain assessment in neonatal intensive care units: The impact of video-based neonatal pain assessment in real-world scenarios on pain diagnosis and its artificial intelligence application. Diagnostics, 13(16), 2661. https://doi.org/10.3390/diagnostics13162661
  • 10. Coşkun, A. B., Kenner, C., Şahiner, N. C., & Elmaoğlu, E. (2025). Neonatal intensive care nurses’ perceptions of artificial intelligence integration in neonatal skin assessment: A qualitative phenomenological study. Advances in Skin & Wound Care, 38(9), 496–503. https://doi.org/10.1097/ASW.0000000000000345
  • 11. Erdoğan Yıldırım, A., & Canayaz, M. (2024). Machine learning-based prediction of length of stay (LoS) in the neonatal intensive care unit using ensemble methods. Neural Computing and Applications, 36(23), 14433–14448. https://doi.org/10.1007/s00521-024-09831-7
  • 12. Feldman, K., & Rohan, A. J. (2022). Data-driven nurse staffing in the neonatal intensive care unit. MCN. The American Journal of Maternal Child Nursing, 47(5), 249–264. https://doi.org/10.1097/NMC.0000000000000839
  • 13. Gogula, S., Khaleel, S., Haritha, H., Sudhakar, S., & Vardhan, C. M. V. (2025). Artificial intelligence for real-time monitoring of neonatal vital signs: Enhancing decision-making in critical care units. Journal of Neonatal Surgery, 14(15s), 2092–2101.
  • 14. Gökçen Gökalp, M., & Üzer, M. A. (2024). Yapay zekâ çağında hemşirelik bakımı [Nursing care in the age of artificial intelligence]. Sağlık Bilimleri Üniversitesi Hemşirelik Dergisi, 6(1), 89–94. https://doi.org/10.48071/sbuhemsirelik.1349981
  • 15. Heiderich, T. M., Carlini, L. P., Buzuti, L. F., Balda, R. D. C. X., Barros, M. C. M., Guinsburg, R., & Thomaz, C. E. (2023). Face-based automatic pain assessment: Challenges and perspectives in neonatal intensive care units. Jornal de Pediatria, 99(6), 546–560. https://doi.org/10.1016/j.jped.2023.05.005
  • 16. Kandemir, F., Azizoğlu, F., & Terzi, B. (2023). Hemşirelikte yapay zekâ ve robot teknolojilerinin kullanımı [The use of artificial intelligence and robotic technologies in nursing]. Yoğun Bakım Hemşireliği Dergisi, 27(2), 118–127.
  • 17. Keleş, E., & Bağcı, U. (2023). The past, current, and future of neonatal intensive care units with artificial intelligence. arXiv. https://doi.org/10.48550/arXiv.2302.00225
  • 18. Kurnaz, E. B., Yiğit, D., & Sezici, E. (2025). Artificial intelligence-based applications used in neonatal intensive care units. In H. Coşkun (Ed.), Human-centered AI applications for medical informatics (pp. 133–164). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3373-1479-2
  • 19. Manworren, R. C. B., Horner, S., Joseph, R., Dadar, P., & Kaduwela, N. (2024). Performance evaluation of a supervised machine learning pain classification model developed by neonatal nurses. Advances in Neonatal Care, 24(3), 301–310. https://doi.org/10.1097/ANC.0000000000001145
  • 20. Meeus, M., Beirnaert, C., Mahieu, L., Laukens, K., Meysman, P., Mulder, A., & Van Laere, D. (2024). Clinical decision support for improved neonatal care: The development of a machine learning model for the prediction of late-onset sepsis and necrotizing enterocolitis. The Journal of Pediatrics, 266, 113869. https://doi.org/10.1016/j.jpeds.2023.113869
  • 21. Nurkalem, S., & Gülseven Karabacak, B. (2025). Hemşirelikte yapay zekâ: Teknolojik devrimin etik ve hümanistik boyutu [Nursing in artificial intelligence: Ethical and humanistic dimensions of the technological revolution]. Sağlık ve Yaşam Bilimleri Dergisi, 7(2), 116–123. https://doi.org/10.33308/2687248X.202572383
  • 22. Özsezer, G. (2022). Hemşirelik alanında yapay zekânın geleceği [The future of artificial intelligence in nursing]. Journal of Human Sciences, 19(2), 285–299. https://doi.org/10.14687/jhs.v19i2.6217
  • 23. Salekin, M. S., Mouton, P. R., Zamzmi, G., Patel, R., Goldgof, D., Kneusel, M., Elkins, S. L., Murray, E., Coughlin, M. E., Maguire, D., Ho, T., & Sun, Y. (2021). Future roles of artificial intelligence in early pain management of newborns. Paediatric & Neonatal Pain, 3(3), 134–145. https://doi.org/10.1002/pne2.12060
  • 24. Schouten, J. S., Kalden, M. A. C. M., Van Twist, E., Reiss, I. K. M., Gommers, D. A. M. P. J., Van Genderen, M. E., & Taal, H. R. (2024). From bytes to bedside: A systematic review on the use and readiness of artificial intelligence in the neonatal and pediatric intensive care unit. Intensive Care Medicine, 50(11), 1767–1777. https://doi.org/10.1007/s00134-024-07629-8
  • 25. Sullivan, B. A., Kausch, S. L., & Fairchild, K. D. (2023). Artificial and human intelligence for early identification of neonatal sepsis. Pediatric Research, 93(2), 350–356. https://doi.org/10.1038/s41390-022-02274-7
  • 26. Tudor, S., Bhatia, R., Liem, M., Wani, T. A., Boyd, J., & Raza Khan, U. (2025). Opportunities and challenges of using artificial intelligence in predicting clinical outcomes and length of stay in neonatal intensive care units: Systematic review. Journal of Medical Internet Research, 27, e63175. https://doi.org/10.2196/63175
  • 27. Ünal, A. S., & Avcı, A. (2024). Evaluation of neonatal nurses’ anxiety and readiness levels towards the use of artificial intelligence. Journal of Pediatric Nursing, 79, 16–23. https://doi.org/10.1016/j.pedn.2024.09.012
  • 28. Varisco, G., Peng, Z., Kommers, D., Zhan, Z., Cottaar, W., Andriessen, P., Long, X., & Van Pul, C. (2022). Central apnea detection in premature infants using machine learning. Computer Methods and Programs in Biomedicine, 226, 107155. https://doi.org/10.1016/j.cmpb.2022.107155
  • 29. Ventura-Silva, J., Martins, M. M., Trindade, L. 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. https://doi.org/10.3390/nursrep14040202
  • 30. Wang, H., & Zhou, R. (2021). The application of blockchain to electronic health record systems: A review. In 2021 International Conference on Information Technology and Biomedical Engineering (ICITBE) (pp. 397–401). IEEE. https://doi.org/10.1109/ICITBE54178.2021.00092
  • 31. World Health Organization. (2022). WHO recommendations for care of the preterm or low-birth-weight infant. Retrieved September 25, 2025, from https://www.ncbi.nlm.nih.gov/books/NBK586710/
  • 32. Xin, Y., Tang, X., Zhou, R., Lv, X., Ma, Y., Shen, J., Qin, Y., Sha, S., & Qian, J. (2025). Descriptor: Neonatal intensive care unit fine-grained care video dataset (NICU-Care). IEEE Data Descriptions, 2, 203–210. https://doi.org/10.1109/IEEEDATA.2025.3576054
  • 33. Yiğit, D., & Açıkgöz, A. (2024). Evaluation of comfort behavior levels of newborn by artificial intelligence techniques. Journal of Perinatal & Neonatal Nursing, 38(3), E38–E45. https://doi.org/10.1097/JPN.0000000000000768

Yenidoğan Yoğun Bakım Ünitesinde Yapay Zeka Uygulamaları ve Hemşirelik Açısından Değerlendirilmesi

Yıl 2026, Cilt: 5 Sayı: 1, 49 - 56, 24.03.2026
https://doi.org/10.61830/balkansbd.1844500
https://izlik.org/JA53FW84MD

Öz

Yapay zeka teknolojilerindeki gelişmeler, yenidoğan yoğun bakım ünitelerinde sunulan bakım hizmetlerinin niteliğini artırma potansiyeline sahiptir ve hemşirelik uygulamalarında önemli dönüşümlere yol açmaktadır. Bu derlemenin amacı, yenidoğan yoğun bakım ünitesinde kullanılan yapay zeka temelli uygulamaları güncel literatür ışığında inceleyerek bu uygulamaların hemşirelik bakımı üzerindeki etkilerini değerlendirmektir. Derleme kapsamında, yapay zekanın sürekli izlem ve monitörizasyon, klinik karar destek sistemleri, apne ve sepsis gibi yaşamı tehdit eden durumların erken tanılanması, yenidoğan ağrı ve konforunun değerlendirilmesi, hastanede kalış süresinin öngörülmesi ve hemşirelik iş yükünün yönetimi gibi alanlardaki kullanımı ele alınmıştır. İncelenen çalışmalar, yapay zeka destekli sistemlerin büyük ve karmaşık veri setlerini analiz ederek erken uyarı mekanizmalarını güçlendirdiğini, yanlış alarm oranlarını azalttığını ve hasta güvenliğinin artırılmasına katkı sağladığını göstermektedir. Bununla birlikte, mevcut çalışmaların büyük bölümünün prototip ve model geliştirme aşamasında olduğu ve klinik uygulamaya entegrasyonun sınırlı kaldığı görülmektedir. Ayrıca, yapay zekanın hemşirelik bakımına entegrasyonu; etik sorumluluk, mesleki özerklik, veri gizliliği ve insan merkezli bakımın korunması gibi önemli tartışmaları beraberinde getirmektedir. Sonuç olarak, yapay zeka uygulamaları yenidoğan yoğun bakım ünitesinde hemşirelik bakımını destekleyen güçlü bir araçtır; ancak güvenli ve etkili kullanım için eğitim, kurumsal altyapı ve etik rehberlerin güçlendirilmesine ihtiyaç vardır.

Etik Beyan

Bu çalışma literatür taramasına dayalı olduğu için etik kurul onayı gerektirmemektedir.

Destekleyen Kurum

Herhangi bir kurumdan destek alınmamıştır.

Kaynakça

  • Kaynakça 1. Alıcılar, H. E., & Çöl, M. (2021). Halk sağlığında yapay zekânın kullanımı [The use of artificial intelligence in public health]. Uludağ Üniversitesi Tıp Fakültesi Dergisi, 47(1), 151–158. https://doi.org/10.32708/uutfd.891274
  • 2. Al Khatib, I., & Ndiaye, M. (2025). Examining the role of AI in changing the role of nurses in patient care: A systematic review. JMIR Nursing, 8, e63335. https://doi.org/10.2196/63335
  • 3. American Nurses Association. (2025). What is AI and how is it applied in nursing practice? Retrieved September 25, 2025, from https://www.nursingworld.org/globalassets/practiceandpolicy/nursing-excellence/ana-position-statements/the-ethical-use-of-artificial-intelligence-in-nursing-practice_bod-approved
  • 4. Ayed, A., Batran, A., Aqtam, I., Malak, M. Z., Ejheisheh, M. A., Farajallah, M., Farraj, L., & Alkhatib, S. (2025). Perceived worries in the adoption of artificial intelligence among nurses in neonatal intensive care units. BMC Nursing, 24(1), 777. https://doi.org/10.1186/s12912-025-03318-z
  • 5. Beam, K., Sharma, P., Levy, P., & Beam, A. L. (2024). Artificial intelligence in the neonatal intensive care unit: The time is now. Journal of Perinatology, 44(1), 131–135. https://doi.org/10.1038/s41372-023-01719-z
  • 6. Bodur, G., Çakır, H., Turan, S., Seren, A. K. H., & Göktaş, P. (2025). Artificial intelligence in nursing practice: A qualitative study of nurses’ perspectives on opportunities, challenges, and ethical implications. BMC Nursing, 24(1), 1263. https://doi.org/10.1186/s12912-025-03775-6
  • 7. Carlini, L. P., Coutrin, G. D. A. S., Ferreira, L. A., Soares, J. D. C. A., Silva, G. V. T., Heiderich, T. M., Balda, R. D. C. X., Barros, M. C. D. M., Guinsburg, R., & Thomaz, C. E. (2024). Human vs machine towards neonatal pain assessment: A comprehensive analysis of the facial features extracted by health professionals, parents, and convolutional neural networks. Artificial Intelligence in Medicine, 147, 102724. https://doi.org/10.1016/j.artmed.2023.102724
  • 8. Chen, B., Alrifai, W., Gao, C., Jones, B., Novak, L., Lorenzi, N., France, D., Malin, B., & Chen, Y. (2021). Mining tasks and task characteristics from electronic health record audit logs with unsupervised machine learning. Journal of the American Medical Informatics Association, 28(6), 1168–1177. https://doi.org/10.1093/jamia/ocaa338
  • 9. Chen, X., Zhu, H., Mei, L., Shu, Q., Cheng, X., Luo, F., Zhao, Y., Chen, S., & Pan, Y. (2023). Video-based versus on-site neonatal pain assessment in neonatal intensive care units: The impact of video-based neonatal pain assessment in real-world scenarios on pain diagnosis and its artificial intelligence application. Diagnostics, 13(16), 2661. https://doi.org/10.3390/diagnostics13162661
  • 10. Coşkun, A. B., Kenner, C., Şahiner, N. C., & Elmaoğlu, E. (2025). Neonatal intensive care nurses’ perceptions of artificial intelligence integration in neonatal skin assessment: A qualitative phenomenological study. Advances in Skin & Wound Care, 38(9), 496–503. https://doi.org/10.1097/ASW.0000000000000345
  • 11. Erdoğan Yıldırım, A., & Canayaz, M. (2024). Machine learning-based prediction of length of stay (LoS) in the neonatal intensive care unit using ensemble methods. Neural Computing and Applications, 36(23), 14433–14448. https://doi.org/10.1007/s00521-024-09831-7
  • 12. Feldman, K., & Rohan, A. J. (2022). Data-driven nurse staffing in the neonatal intensive care unit. MCN. The American Journal of Maternal Child Nursing, 47(5), 249–264. https://doi.org/10.1097/NMC.0000000000000839
  • 13. Gogula, S., Khaleel, S., Haritha, H., Sudhakar, S., & Vardhan, C. M. V. (2025). Artificial intelligence for real-time monitoring of neonatal vital signs: Enhancing decision-making in critical care units. Journal of Neonatal Surgery, 14(15s), 2092–2101.
  • 14. Gökçen Gökalp, M., & Üzer, M. A. (2024). Yapay zekâ çağında hemşirelik bakımı [Nursing care in the age of artificial intelligence]. Sağlık Bilimleri Üniversitesi Hemşirelik Dergisi, 6(1), 89–94. https://doi.org/10.48071/sbuhemsirelik.1349981
  • 15. Heiderich, T. M., Carlini, L. P., Buzuti, L. F., Balda, R. D. C. X., Barros, M. C. M., Guinsburg, R., & Thomaz, C. E. (2023). Face-based automatic pain assessment: Challenges and perspectives in neonatal intensive care units. Jornal de Pediatria, 99(6), 546–560. https://doi.org/10.1016/j.jped.2023.05.005
  • 16. Kandemir, F., Azizoğlu, F., & Terzi, B. (2023). Hemşirelikte yapay zekâ ve robot teknolojilerinin kullanımı [The use of artificial intelligence and robotic technologies in nursing]. Yoğun Bakım Hemşireliği Dergisi, 27(2), 118–127.
  • 17. Keleş, E., & Bağcı, U. (2023). The past, current, and future of neonatal intensive care units with artificial intelligence. arXiv. https://doi.org/10.48550/arXiv.2302.00225
  • 18. Kurnaz, E. B., Yiğit, D., & Sezici, E. (2025). Artificial intelligence-based applications used in neonatal intensive care units. In H. Coşkun (Ed.), Human-centered AI applications for medical informatics (pp. 133–164). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3373-1479-2
  • 19. Manworren, R. C. B., Horner, S., Joseph, R., Dadar, P., & Kaduwela, N. (2024). Performance evaluation of a supervised machine learning pain classification model developed by neonatal nurses. Advances in Neonatal Care, 24(3), 301–310. https://doi.org/10.1097/ANC.0000000000001145
  • 20. Meeus, M., Beirnaert, C., Mahieu, L., Laukens, K., Meysman, P., Mulder, A., & Van Laere, D. (2024). Clinical decision support for improved neonatal care: The development of a machine learning model for the prediction of late-onset sepsis and necrotizing enterocolitis. The Journal of Pediatrics, 266, 113869. https://doi.org/10.1016/j.jpeds.2023.113869
  • 21. Nurkalem, S., & Gülseven Karabacak, B. (2025). Hemşirelikte yapay zekâ: Teknolojik devrimin etik ve hümanistik boyutu [Nursing in artificial intelligence: Ethical and humanistic dimensions of the technological revolution]. Sağlık ve Yaşam Bilimleri Dergisi, 7(2), 116–123. https://doi.org/10.33308/2687248X.202572383
  • 22. Özsezer, G. (2022). Hemşirelik alanında yapay zekânın geleceği [The future of artificial intelligence in nursing]. Journal of Human Sciences, 19(2), 285–299. https://doi.org/10.14687/jhs.v19i2.6217
  • 23. Salekin, M. S., Mouton, P. R., Zamzmi, G., Patel, R., Goldgof, D., Kneusel, M., Elkins, S. L., Murray, E., Coughlin, M. E., Maguire, D., Ho, T., & Sun, Y. (2021). Future roles of artificial intelligence in early pain management of newborns. Paediatric & Neonatal Pain, 3(3), 134–145. https://doi.org/10.1002/pne2.12060
  • 24. Schouten, J. S., Kalden, M. A. C. M., Van Twist, E., Reiss, I. K. M., Gommers, D. A. M. P. J., Van Genderen, M. E., & Taal, H. R. (2024). From bytes to bedside: A systematic review on the use and readiness of artificial intelligence in the neonatal and pediatric intensive care unit. Intensive Care Medicine, 50(11), 1767–1777. https://doi.org/10.1007/s00134-024-07629-8
  • 25. Sullivan, B. A., Kausch, S. L., & Fairchild, K. D. (2023). Artificial and human intelligence for early identification of neonatal sepsis. Pediatric Research, 93(2), 350–356. https://doi.org/10.1038/s41390-022-02274-7
  • 26. Tudor, S., Bhatia, R., Liem, M., Wani, T. A., Boyd, J., & Raza Khan, U. (2025). Opportunities and challenges of using artificial intelligence in predicting clinical outcomes and length of stay in neonatal intensive care units: Systematic review. Journal of Medical Internet Research, 27, e63175. https://doi.org/10.2196/63175
  • 27. Ünal, A. S., & Avcı, A. (2024). Evaluation of neonatal nurses’ anxiety and readiness levels towards the use of artificial intelligence. Journal of Pediatric Nursing, 79, 16–23. https://doi.org/10.1016/j.pedn.2024.09.012
  • 28. Varisco, G., Peng, Z., Kommers, D., Zhan, Z., Cottaar, W., Andriessen, P., Long, X., & Van Pul, C. (2022). Central apnea detection in premature infants using machine learning. Computer Methods and Programs in Biomedicine, 226, 107155. https://doi.org/10.1016/j.cmpb.2022.107155
  • 29. Ventura-Silva, J., Martins, M. M., Trindade, L. 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. https://doi.org/10.3390/nursrep14040202
  • 30. Wang, H., & Zhou, R. (2021). The application of blockchain to electronic health record systems: A review. In 2021 International Conference on Information Technology and Biomedical Engineering (ICITBE) (pp. 397–401). IEEE. https://doi.org/10.1109/ICITBE54178.2021.00092
  • 31. World Health Organization. (2022). WHO recommendations for care of the preterm or low-birth-weight infant. Retrieved September 25, 2025, from https://www.ncbi.nlm.nih.gov/books/NBK586710/
  • 32. Xin, Y., Tang, X., Zhou, R., Lv, X., Ma, Y., Shen, J., Qin, Y., Sha, S., & Qian, J. (2025). Descriptor: Neonatal intensive care unit fine-grained care video dataset (NICU-Care). IEEE Data Descriptions, 2, 203–210. https://doi.org/10.1109/IEEEDATA.2025.3576054
  • 33. Yiğit, D., & Açıkgöz, A. (2024). Evaluation of comfort behavior levels of newborn by artificial intelligence techniques. Journal of Perinatal & Neonatal Nursing, 38(3), E38–E45. https://doi.org/10.1097/JPN.0000000000000768
Toplam 33 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Çocuk Sağlığı ve Hastalıkları Hemşireliği
Bölüm Derleme
Yazarlar

Gülsev Kutman 0000-0003-3560-1001

Gönderilme Tarihi 18 Aralık 2025
Kabul Tarihi 1 Şubat 2026
Yayımlanma Tarihi 24 Mart 2026
DOI https://doi.org/10.61830/balkansbd.1844500
IZ https://izlik.org/JA53FW84MD
Yayımlandığı Sayı Yıl 2026 Cilt: 5 Sayı: 1

Kaynak Göster

APA Kutman, G. (2026). Yenidoğan Yoğun Bakım Ünitesinde Yapay Zeka Uygulamaları ve Hemşirelik Açısından Değerlendirilmesi. Balkan Sağlık Bilimleri Dergisi, 5(1), 49-56. https://doi.org/10.61830/balkansbd.1844500

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