Pediatri Hemşireliğinde Yapay Zekâ Destekli Yaklaşımlar

Öz

Yapay zekâ (YZ), insan zekâsını taklit eden sistemler aracılığıyla sağlık hizmetlerinde dönüşüm yaratmaktadır. Hemşirelik alanında, özellikle pediatri hemşireliğinde yapay zekâ kullanımı son yıllarda hız kazanmış; klinik karar destek sistemlerinden, uzaktan hasta takibine kadar geniş bir yelpazede yerini almıştır. Bu gelişmeler umut verici fırsatlar yaratırken aynı zamanda etik, mesleki ve teknik kaygıları beraberinde getirmektedir. Pediatri hemşireliğinde yapay zekâ teknolojilerinin kullanımı hasta bakım kalitesini artırma, erken müdahale ve hemşirelik uygulamalarında destekleyici bir rol oynama potansiyeli taşımaktadır. Ancak hemşirelerin bu dönüşüme hazırlanması, etik ve güvenlik konularının dikkatli bir şekilde ele alınması ve mesleğin insani yönlerinin korunması gereklidir. Yapay zekâ kullanımını ölçmek; klinik ortamlarda yapay zekâ araçlarının yaygınlığı, sağlık profesyonelleri arasında yapay zekâya yönelik eğitim düzeyi ve yapay zekânın hasta bakımına entegre edilme derecesi gibi faktörlerin değerlendirilmesini gerektirdiğinden zor olabilir. Dolayısıyla, sağlık hizmetlerinde yapay zekânın rolünün ve etkisinin daha iyi ölçülebilmesi ancak standartlaştırılmış ölçütler geliştirmek için daha fazla araştırmanın yapılmasıyla mümkündür. Literatürde yapay zekânın yüksek kaliteli bakımı geliştirme, kişiselleştirilmiş bakım ihtiyaçlarını belirleme, insan hatasını izleme ve en aza indirme, bakım maliyetlerini azaltma potansiyelini vurgulayan çalışmalar mevcuttur. Bu literatür derlemesinin amacı; pediatri hemşireliğinde Yapay zekâ kullanım durumu ve yapay zekâ uygulamalarını güncel literatür ışığında değerlendirmektir.

Anahtar Kelimeler

• Yapay zeka
• pediatri
• hemşirelik bakımı

Artificial Intelligence Supported Approaches in Pediatric Nursing

Öz

Artificial intelligence (AI) is transforming healthcare services through systems that mimic human intelligence. The use of AI in the field of nursing, especially in pediatric nursing, has gained momentum in recent years; it has taken its place in a wide range from clinical decision support systems to remote patient monitoring. These developments create both promising opportunities and some ethical, professional and technical concerns. The use of artificial intelligence technologies in pediatric nursing has the potential to improve the quality of patient care, early intervention and play a supportive role in nursing practices. However, it is necessary to prepare nurses for this transformation, to address ethical and safety issues carefully, and to protect the human aspects of the profession. Measuring the use of AI can be challenging as it requires the assessment of factors such as the prevalence of AI tools in clinical settings, the level of training in AI among healthcare professionals, and the degree to which AI is integrated into patient care. Therefore, a better measurement of the role and impact of AI in healthcare is only possible with further research to develop standardised metrics. There are studies in the literature highlighting the potential of AI to improve high quality care, identify personalised care needs, monitor and minimise human error, and reduce care costs. The aim of this literature review is to evaluate the use of artificial intelligence in pediatric nursing and artificial intelligence applications in the light of current literature

Anahtar Kelimeler

• Artificial intelligence
• pediatrics
• nursing care

Kaynakça

1. Özçevik Subaşı D, Akça Sümengen A, Semerci R, Şimşek E, Çakır GN, Temizsoy E. Pediatric nurses’ perspectives on artificial intelligence applications: A cross‐sectional study of concerns, literacy levels and attitudes. J Adv Nurs. 2025; 81(3):1353-1363.
2. Beedholm K, Frederiksen K, Lomborg K. What was (also) at stake when a robot bathtub was implemented in a Danish elder center: A constructivist secondary qualitative analysis. Qual Health Res. 2016; 26(10):1424-1433.
3. Pailaha AD. The impact and issues of artificial intelligence in nursing science and healthcare settings. SAGE Open Nurs. 2023; 9: 23779608231196847.
4. Johnson DG, Verdicchio M. AI anxiety. J Assoc Inf Sci Technol. 2017; 68(9): 2267-2270.
5. Castagno S, Khalifa M. Perceptions of artificial intelligence among healthcare staff: A Qualitative Survey Study. Front Artif Intell. 2020; 3: 84.
6. Abdullah R, Fakieh B. Health care employees’ perceptions of the use of artificial intelligence applications: Survey study. J Med Internet Res. 2020; 22(5): e17620.
7. Wang X, Fei F, Wei J, Huang M, Xiang F, Tu J, et al. Knowledge and attitudes toward artificial intelligence in nursing among various categories of professionals in China: a cross-sectional study. Front Public Health. 2024; 12: 1433252.
8. Murff HJ, FitzHenry F, Matheny ME, Gentry N, Kotter KL, Crimin K, ... Speroff T. Automated identification of postoperative complications within an electronic medical record using natural language processing. JAMA, 2011; 306(8): 848-855.

9. Darcy AM, Louie AK, Roberts LW. Machine learning and the profession of medicine. JAMA. 2016; 315(6): 551-552.
10. Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: Past, present and future. Stroke Vasc Neurol. 2017; 2(4):230-243.
11. Karakülah G, Dicle O, Koşaner Ö, Suner A, Birant ÇC, Berber T, ve ark. Computer based extraction of phenoptypic features of human congenital anomalies from the digital literature with natural language processing techniques. In e-Health–For Continuity of Care. IOS Press. 2014; 570-574.
12. Liang HF, Wu KM, Weng CH, Hsieh HW. Nurses’ views on the potential use of robots in the pediatric unit. J Pediatr Nurs. 2019; 47: e58-e64.
13. Amann J, Vetter D, Blomberg SN, Christensen HC, Coffee M, Gerke S, et al. To explain or not to explain?—Artificial intelligence explainability in clinical decision support systems. PLOS Digit Health. 2022; 1(2): e0000016.
14. Zhao Y, Hu J, Gu Y, Wan Y, Liu F, Ye C, et al. Development and implementation of a pediatric nursing-clinical decision support system for hyperthermia: A Pre-and post-test. CIN: Comput Inform Nurs. 2022; 40(2), 131-137.
15. Ünal AS, Avcı A. Evaluation of neonatal nurses’ anxiety and readiness levels towards the use of artificial intelligence. J Pediatr Nurs. 2024a; 79, e16-e23.
16. Kim Y. Pediatric nursing in the AI era: from clinical integration to ethical practice to education. Child Health Nurs Res. 2025; 31(3), 131.
17. Zhang W, Xiong K, Zhu C, Evans R, Zhou L, Podrini C. Promoting child and adolescent health through wearable technology: A systematic review. Digit Health. 2024; 10, 1-11.
18. Ünal AS, Avcı A. Pediatri hemşireliğinde yapay zeka. Akd Hemsirelik D. 2024b; 3(1): 36-43.
19. Foster C, Schinasi D, Kan K, Lantos JD. Remote monitoring of patient- and family-generated health data in pediatrics. Pediatrics. 2022; 149(2), e2021054137.
20. Kerth JL, Hagemeister M, Bischops AC, Reinhart L, Dukart J, Heinrichs B, Eickhoff SB, Meissner T. Artificial intelligence in the care of children and adolescents with chronic diseases: A systematic review. Eur J Pediatr. 2025; 184(1): 83.
21. Zama D, Borghesi A, Ranieri A, Manieri E, Pierantoni L, Andreozzi L, et al. Perspectives and challenges of telemedicine and artificial intelligence in pediatric dermatology. Children. 2024; 11(11): 1401.
22. Ferstad JO, Vallon JJ, Jun D, Gu A, Vitko A, Morales DP, et al. Population-level management of type 1 diabetes via continuous glucose monitoring and algorithm-enabled patient prioritization: Precision health meets population health. Pediatr Diabetes. 2024; 25(1): 75–85.
23. Wolf RM, Channa R, Liu TYA, Zehra A, Bromberger L, Patel D, et al. Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: The ACCESS randomized control trial. Nat Commun. 2024; 15(1): 421.
24. Edwards J, Waite-Jones J, Schwarz T, Swallow V. Digital technologies for children and parents sharing self-management in childhood chronic or long-term conditions: A scoping review. Children. 2021; 8(12): 1203.
25. Ulak SK, Başbakkal Z. Nesnelerin interneti ve pediatrik bakımdaki önemi. Eurasian J Health Technol Assess. 2024; 8(2): 84-98.
26. WHO, 2019. WHO guideline: recommendations on digital interventions for health system strengthening. Geneva: World Health Organization Licence: CC BY-NC-SA 3.0 IGO. Available from: https://www.who.int/publications/i/ item/WHO-RHR-19.8 [Accessed 15th June 2025].
27. Karaarslan D, Kahraman A, Ergin E. How does training given to pediatric nurses about artificial intelligence and robot nurses affect their opinions and attitude levels? A quasi-experimental study. J Pediatr Nurs. 2024; 77, e211-e217.
28. Yang RL, Yang YL, Wang T, Xu WZ, Yu G, Yang JB, et al. Establishment of an auxiliary diagnosis system of newborn screening for inherited metabolic diseases based on artificial intelligence technology and a clinical trial. Zhonghua er ke za zhi= Chinese Journal of Pediatrics. 2021; 59(4): 286-293.
29. Pavel AM, O’Toole JM, Proietti J, Livingstone V, Mitra S, Marnane WP, et al. Machine learning for the early prediction of infants with electrographic seizures in neonatal hypoxic‐ischemic encephalopathy. Epilepsia. 2022; 64(2): 456- 468.
30. Raimondi F, Migliaro F, Verdoliva L, Gragnaniello D, Poggi G, Kosova R, et al. Visual assessment versus computer-assisted gray scale analysis in the ultrasound evaluation of neonatal respiratory status. PLoS One. 2018; 13(10): e0202397.
31. Redd TK, Campbell JP, Brown JM, Kim SJ, Ostmo S, Chan RVP, et al. Evaluation of a deep learning image assessment system for detecting severe retinopathy of prematurity. Br J Ophthalmol. 2019; 103(5): 580-584.
32. Lyra S, Rixen J, Heimann K, Karthik S, Joseph J, Jayaraman K, et al. Camera fusion for real-time temperature monitoring of neonates using deep learning. Med Biol Eng Comput. 2022; 60(6): 1787-1800.
33. Chaichulee S, Villarroel M, Jorge J, Arteta C, McCormick K, Zisserman A, et al. Cardio-respiratory signal extraction from video camera data for continuous non-contact vital sign monitoring using deep learning. Physiol Meas. 2019; 40(11): 115001.
34. Chioma R, Sbordone A, Patti ML, Perri A, Vento G, Nobile S. Applications of artificial intelligence in neonatology. Appl Sci. 2023; 13(5): 3211.
35. Yigit D, Acikgoz A. Evaluation of comfort behavior levels of newborn by artificial intelligence techniques. J Perinat Neonatal Nurs. 2024; 38(3), E38-E45.
36. Carroll W. Artificial intelligence, nurses and the quadruple aim. Online Journal of Nursing Informatics. 2018; 22(2).
37. Al-Sofyani KA. Role of artificial intelligence in pediatric intensive care: A survey of healthcare staff perspectives in Saudi Arabia. Front Pediatr. 2025; 13: 1533877.
38. Harmon J, Pitt V, Summons P, Inder KJ. Use of artificial intelligence and virtual reality within clinical simulation for nursing pain education: A scoping review. Nurse Educ Today. 2021; 97: 104700.
39. Klann JG, Anand V, Downs SM. Patient-tailored prioritization for a pediatric care decision support system through machine learning. J Am Med Inform Assoc. 2013; 20(e2): e267-e274.
40. Carlin CS, Ho LV, Ledbetter DR, Aczon MD, Wetzel RC. Predicting individual physiologically acceptable states at discharge from a pediatric intensive care unit. J Am Med Inform Assoc. 2018; 25(12): 1600-1607.
41. Or XY, Ng YX, Goh YS. Effectiveness of social robots in improving psychological well-being of hospitalised children: A systematic review and meta-analysis. J Pediatr Nurs. 2025; 82: 11-20.
42. Merih YD, Akdoğan E. Hemşirelikte Yapay Zekâ. In 4th International Eurasian Conference on Biological and Chemical Sciences (EurasianBio- Chem 2021) November. 2021; 24-26.
43. Vasquez B, Moreno‐Lacalle R, Soriano GP, Juntasoopeepun P, Locsin RC, Evangelista LS. Technological machines and artificial intelligence in nursing practice. Nurs Health Sci. 2023; 25(3): 474-481.
44. Papadopoulos I, Koulouglioti C. The influence of culture on attitudes towards humanoid and animal‐like robots: An Integrative Review. J Nurs Scholarsh. 2018; 50(6): 653-665.
45. Venu N, ArunKumar DA, Vaigandla KK. Investigation on internet of things (IoT): Technologies, challenges and applications in healthcare. International Journal of Research. 2022; 11(2): 143-153.
46. Zeadally S, Siddiqui F, Baig Z, Ibrahim A. Smart healthcare: Challenges and potential solutions using internet of things (IoT) and big data analytics. PSU Res. Rev. 2020; 4(2): 149-168.
47. Buchanan C, Howitt ML, Wilson R, Booth RG, Risling T, Bamford M. Predicted influences of artificial intelligence on nursing education: Scoping review. JMIR Nurs. 2021; 4(1): e23933.
48. De Gagne JC. The state of artificial intelligence in nursing education: Past, present, and future directions. Int J Environ Res Public Health. 2023; 20(6): 4884.
49. Glauberman G, Ito-Fujita A, Katz S, Callahan J. Artificial intelligence in nursing education: Opportunities and challenges. Hawaii J Health Soc Welf. 2023; 82(12): 302–305.