Clinical and Experimental Pediatrics

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...

DOI: https://doi.org/10.3345/cep.2025.00962 [Accepted]

PDF

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

Web of Science 11 Crossref 7

· 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.

DOI: https://doi.org/10.3345/cep.2020.01333

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

Web of Science 8 Crossref 10

· 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.

DOI: https://doi.org/10.3345/cep.2021.00766

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

Web of Science 4 Crossref 4

· 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.

DOI: https://doi.org/10.3345/cep.2021.01438

Cited By 26	Neurodevelopmental outcomes of preterm infants
Cited By 21	Long COVID in children and adolescents: prevalence, clinical manifestations, and management strategies
Cited By 20	Association of gut microbiota with obesity in children and adolescents
Cited By 19	Prevalence of anxiety, depression, and stress among parents of neonates admitted to neonatal intensive care unit: a systematic review and meta-analysis
Cited By 18	Gut microbiota affects brain development and behavior

Search