Artificial Intelligence in Pediatric Healthcare Part I: Foundations and Basic Concepts

Abstract

Artificial intelligence (AI) is rapidly transforming healthcare by enabling machines to analyze complex datasets, recognize patterns, and support clinical decision-making. Advances in machine learning (ML), deep learning (DL), and computational technologies, along with the widespread digitization of health records, have accelerated the integration of AI into medical research and clinical practice. In pediatric healthcare, AI has emerging applications in developmental screening, growth and nutrition monitoring, early disease detection, medical imaging interpretation, and prediction of clinical deterioration, particularly in neonatal and pediatric intensive care settings. However, the safe and effective use of AI in pediatric practice requires a clear understanding of its fundamental concepts, methodologies, data sources, and evaluation processes. This narrative review summarizes the foundational concepts of AI relevant to medicine and pediatrics, including the historical evolution of AI in healthcare, core methodologies, types of machine learning models, major clinical data sources, and the development pipeline of medical AI systems. This article is the first in a four-part series on AI in pediatrics; subsequent articles will discuss AI applications in ambulatory pediatrics, hospital and critical care settings, and the ethical and regulatory considerations related to the use of AI in child health.