Current Use and Prospects of Artificial Intelligence in Pediatrics and Pediatric Intensive Care
Keywords:
artificial intelligence; machine learning; pediatrics; pediatric intensive care; clinical prediction.Abstract
Introduction: Artificial intelligence is progressively being incorporated into pediatric medicine, especially in pediatric intensive care units, where the volume of continuous data coming from physiological monitors, mechanical ventilators, laboratory results, and electronic clinical records exceeds the human capacity for real-time interpretation.
Objective: To describe the current panorama, recent applications, limitations, and future prospects of the use of artificial intelligence in pediatric intensive care.
Methods: A narrative review of the scientific literature published from January 2023 to May 2025, in English and Spanish, was carried out in the PubMed/MEDLINE, SpringerLink, Nature, MDPI, JMIR, arXiv, and Medicina Intensiva databases. Initially, two hundred eighty-three articles were identified. After eliminating duplicates and applying inclusion and exclusion criteria, 29 relevant studies were finally analyzed.
Results: The studies reviewed show applications of artificial intelligence in the prediction of length of hospital stay, early detection of pediatric cardiac arrest, remote patient monitoring, point-of-care ultrasound assistance, and clinical decision support systems.
Conclusions: Artificial intelligence represents an emerging tool with the potential to transform pediatric intensive care medicine. However, clinical implementation requires multicenter validation, algorithmic transparency, training of health personnel and specific regulatory frameworks for the pediatric population.
Downloads
References
1. Shah N, Arshad A, Mazer MB, Carroll CL, Shein SL, Remy KE, et al. The use of machine learning and artificial intelligence within pediatric critical care. Pediatr Res. 2023;93:405-12. DOI: https://doi.org/10.1038/s41390-022-02380-6
2. Campos-Miño S, Sasbón JS, von Dessauer B. Pediatric intensive care in Latin America. Med Intensiva. 2012;36(1):3-10. DOI: https://doi.org/10.1016/j.medine.2012.02.004
3. Schouten JS, Kalden MACM, van Twist E, Reiss IKM, Gommers DAMPJ, van Genderen ME, et al. 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 Med. 2024;50:1767-77. DOI: https://doi.org/10.1007/s00134-024-07629-8
4. Ganatra HA, Latifi SQ, Baloglu O. Pediatric intensive care unit length of stay prediction by machine learning. Bioengineering. 2024;11(10):962. DOI: https://doi.org/10.3390/bioengineering11100962
5. Abdallah ABAA, Sadaka SIH, Ali EI, Bilal SAM, Abdelrahman MO, Mohammed FBF, et al. The role of artificial intelligence in pediatric intensive care: a systematic review. Cureus. 2025;17(3):e80142. DOI: https://doi.org/10.7759/cureus.80142
6. Strutz S, McCracken CE, Rahman A, Hawkins RB, Tzeng YL, Zhang H, et al. Machine learning for predicting critical events among hospitalized children. JAMA Netw Open. 2025;8(5):e2513149. DOI: https://doi.org/10.1001/jamanetworkopen.2025.131495
7. Jia Z, Shi Y, Hu J, Yang L, Nti B. Development of a real-time POCUS image quality assessment and acquisition guidance system [preprint]. arXiv. 2022. DOI: https://doi.org/10.48550/arXiv.2212.08624
8. Chanci D, Grunwell JR, Rafiei A, Brown SR, Ripple MJ, Bishop NR, et al. Machine learning model for daily prediction of pediatric sepsis using Phoenix criteria. Pediatr Res. 2026;99(2):631-7. DOI: https://doi.org/10.1038/s41390-025-04221-8
9. Barrett R, Lawler B, Liu S, Park WY, Davoodi M, Martin B, et al. Transforming neonatal care through informatics: a review of artificial intelligence, data, and implementation considerations. Semin Perinatol. 2025;49:152144. DOI: https://doi.org/10.1016/j.semperi.2025.152144
10. Keles E, Bagci U. The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review. NPJ Digit Med. 2023;6:220. DOI https://doi.org/10.1038/s41746-023-00941-5
11. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56. DOI: https://doi.org/10.1038/s41591-018-0300-7
12. Richter F, Holmes E, Richter F, Guttmann K, Duong SQ, Gangadharan S, et al. Toward governance of artificial intelligence in pediatric healthcare. NPJ Digit Med. 2025;8:636. DOI: https://doi.org/10.1038/s41746-025-02000-7
13. Choudhury A, Urena E. Artificial intelligence in NICU and PICU: a need for ecological validity, accountability, and human factors. Healthcare (Basel). 2022;10(5):952. DOI: https://doi.org/10.3390/healthcare10050952
14. Tan X, Zhang X, Chai J, Ji W, Ru J, Yang C, et al. Constructing a predictive model for early-onset sepsis in neonatal intensive care unit newborns based on SHapley additive explanations explainable machine learning. Transl Pediatr. 2024;13(11):1933-46. DOI: https://doi.org/10.21037/tp-24-278
15. Suberviola B, Gómez-Tello V, Castellanos-Ortega Á. Inteligencia artificial e Internet
of Medical Things en la UCI. Med Intensiva. 2024;48(3):183-93. DOI: https://doi.org/10.1016/j.medin.2023.11.003
16. Mittelstadt BD, Allo P, Taddeo M, Wachter S, Floridi L. The ethics of algorithms: mapping the debate. Big Data Soc. 2016;3(2). DOI: https://doi.org/10.1177/2053951716679679
17. Alizadehsani R, Abdar M, Roshanzamir M, Beykikhoshk A, Khosravi A, Nahavandi S, et al. A review of explainable artificial intelligence in healthcare. Comput Biol Med. 2024;173:109370. DOI: https://doi.org/10.1016/j.compbiomed.2024.109370
18. Esteva A, Robicquet A, Ramsundar B, Kuleshov V, DePristo M, Chou K, et al. A guide to deep learning in healthcare. Nat Med. 2019;25(1):24-9. DOI: https://doi.org/10.1038/s41591-018-0316-z
19. 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. DOI: https://doi.org/10.3389/fped.2025.1533877
20. Park T, Lee Y, Lee J, Kong Y. Artificial intelligence in pediatric healthcare: current applications, potential, and implementation considerations. Clin Exp Pediatr. 2025. DOI: https://doi.org/10.3345/cep.2025.00962
21. Salih AM, Menegaz G, Pillay T, Boyle EM. Explainable artificial intelligence in paediatrics: challenges for the future. Health Sci Rep. 2024;7(12):e70271. DOI: https://doi.org/10.1002/hsr2.70271
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Vivian Rosario Mena Miranda, Vicente Manuel Martínez Cárdenas
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Avisos de derechos de autor propuestos por Creative Commons
1. Política propuesta para revistas que ofrecen acceso abierto
Aquellos autores/as que tengan publicaciones con esta revista, aceptan los términos siguientes:
- Los autores/as conservarán sus derechos de autor y garantizarán a la revista el derecho de primera publicación de su obra, el cuál estará simultáneamente sujeto a la Licencia de reconocimiento de Creative Commons que permite a terceros compartir la obra siempre que se indique su autor y su primera publicación esta revista.
- Los autores/as podrán adoptar otros acuerdos de licencia no exclusiva de distribución de la versión de la obra publicada (p. ej.: depositarla en un archivo telemático institucional o publicarla en un volumen monográfico) siempre que se indique la publicación inicial en esta revista.
- Se permite y recomienda a los autores/as difundir su obra a través de Internet (p. ej.: en archivos telemáticos institucionales o en su página web) antes y durante el proceso de envío, lo cual puede producir intercambios interesantes y aumentar las citas de la obra publicada. (Véase El efecto del acceso abierto).
