Search

  • HOME
  • Search
Review Article
Artificial Intelligence in Pediatric Healthcare: current applications, potentials and implementation considerations
Taejin park, In-Hee Lee, Seung Wook Lee, Sek Won Kong
Artificial intelligence (AI) has transformed pediatric healthcare by supporting diagnostics, personalized treat­ment strategies, and prognosis predictions. Although it offers significant promise in these areas, its application in pediatric settings poses distinct challenges compared with that in adults due to variable developmental status, the limited availability of pediatric data, and ethical con­cerns regarding bias and transparency. This narrative review summarizes the...
Neurology
Recent trends of healthcare information and communication technologies in pediatrics: a systematic review
Se young Jung, Keehyuck Lee, Hee Hwang
Clin Exp Pediatr. 2022;65(6):291-299.   Published online December 15, 2021
· The innovation of healthcare information communication technology (ICT) was accelerated with the adoption of electronic health records (EHRs).
· Telemedicine currently has no technical barriers.
· EHRs and personal health records are being connected, and mobile/wearable technologies are being integrated into them.
· Conventional rule-based clinical decision support systems have already been implemented and used in EHRs and PHRs. Artificial intelligence/machine learning improves precision and accuracy.
Big data analysis and artificial intelligence in epilepsy – common data model analysis and machine learning-based seizure detection and forecasting
Yoon Gi Chung, Yonghoon Jeon, Sooyoung Yoo, Hunmin Kim, Hee Hwang
Clin Exp Pediatr. 2022;65(6):272-282.   Published online November 26, 2021
· Big data analysis, such as common data model and artificial intelligence, can solve relevant questions and improve clinical care.
· Recent deep learning studies achieved 0.887–0.996 areas under the receiver operating characteristic curve for automated interictal epileptiform discharge detection.
· Recent deep learning studies achieved 62.3%–99.0% accuracy for interictal-ictal classification in seizure detection and 75.0%– 87.8% sensitivity with a 0.06–0.21/hr false positive rate in seizure forecasting.
Other
Knowledge-guided artificial intelligence technologies for decoding complex multiomics interactions in cells
Dohoon Lee, Sun Kim
Clin Exp Pediatr. 2022;65(5):239-249.   Published online November 26, 2021
· The need for data-driven modeling of multiomics interactions was recently highlighted.
· Many artificial intelligence-driven models have been developed, but only a few have incorporated biological domain knowledge within model architectures or training procedures.
· Here we provide a comprehensive review of deep learning models to decipher complex multiomics interactions regarding the biological guidance imposed upon them to facilitate further development of biological knowledge-guided deep learning models.


TOPICS

Browse all articles >

ARTICLE CATEGORY

Browse all articles >

BROWSE ARTICLES
FOR CONTRIBUTORS
ABOUT
Editorial Office
Korean Pediatric Society
#1606 Seocho World Officetel, 19 Seoun-ro, Seocho-ku, Seoul 06732, Korea
Tel: +82-2-3473-7306    Fax: +82-2-3473-7307    E-mail: office@e-cep.org                

Clinical and Experimental Pediatrics is an open access journal. All articles are distributed under the terms of the Creative Commons Attribution NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/)

Copyright © 2025 by Korean Pediatric Society.      Developed in M2PI