Applications of artificial intelligence in early childhood health management: a systematic review from fetal to pediatric periods
Abstract
Background: The integration of artificial intelligence (AI) into early childhood health management has expanded rapidly, with applications spanning the fetal, neonatal, and pediatric periods. While numerous studies report promising results, a comprehensive synthesis of AI's performance, methodological quality, and translational readiness in child health is needed.
Objectives: This systematic review aims to evaluate the current landscape of AI applications in fetal and pediatric care, assess their diagnostic accuracy and clinical utility, and identify key barriers to real-world implementation.
Methods: A systematic literature search was conducted in PubMed, Scopus, and Web of Science for studies published between January 2021 and March 2025. Eligible studies involved AI-driven models for diagnosis, prediction, or decision support in individuals aged 0-18 years. Study selection followed the PRISMA 2020 guidelines. Data were extracted on application domain, AI methodology, performance metrics, validation strategy, and clinical integration level.
Results: From 4,938 screened records, 133 studies were included. AI models demonstrated high performance in prenatal anomaly detection (mean AUC: 0.91-0.95), neonatal intensive care (e.g., sepsis prediction with sensitivity up to 89%), and pediatric genetic diagnosis (accuracy: 85%-93% using facial analysis). Deep learning enhanced consistency in fetal echocardiography and ultrasound interpretation. However, 76% of studies used…
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