AI-DRIVEN E-HEALTH INNOVATIONS: IMPACTS ON PEDIATRIC HOSPITAL MANAGEMENT - A LITERATURE REVIEW

Abstract

Artificial intelligence (AI) is transforming pediatric hospital management by improving clinical decision support, diagnostic accuracy, operational efficiency, and patient engagement. This literature review aims to synthesize evidence on the impacts of AI-driven e-health innovations within pediatric hospital settings. A literature search of PubMed, ScienceDirect, and Google Scholar was performed for peer-reviewed studies from the last decade, focusing on AI applications for inpatient and operational pediatric care. Studies were screened based on relevance, language, and pediatric scope (0-18 years old). Analyses encompassed clinical, operational, and ethical impacts, with an emphasis on diagnostic tools, decision support, predictive analytics, and telemedicine. Findings highlight that AI augments early disease detection, supports individualized interventions, and streamlines hospital administration through automation and data-driven forecasting. Enhanced family engagement and remote monitoring expand care accessibility for chronic and geographically dispersed pediatric populations. However, challenges remain, including limited pediatric datasets, algorithmic bias, ethical concerns, and barriers to system integration. In conclusion, AI-driven innovations offer substantive improvements in pediatric hospital management, but realizing their full potential requires collaborative efforts to address safety, transparency, regulatory alignment, and pediatric-specific issues. It is recommended that future research prioritize explainable AI, multicenter data initiatives, and stronger ethical frameworks to guide safe and equitable adoption.