Artificial Intelligence in Pediatric Healthcare - Part II: AI in Ambulatory Preventive Pediatrics

Abstract

Artificial intelligence (AI) is increasingly being incorporated into ambulatory and preventive pediatric practice through advances in machine learning, deep learning, computer vision, natural language processing, and mobile health technologies. The growing availability of electronic health records, wearable devices, telemedicine platforms, and digital health applications has created new opportunities for AI-assisted screening, risk prediction, preventive care, and outpatient clinical decision support. In pediatric healthcare, AI has emerging applications in developmental and autism screening, vision and hearing assessment, growth and nutrition monitoring, vaccination programs, infectious disease surveillance, symptom triage, prescribing support, remote patient monitoring, and parent-facing digital health tools. These technologies have the potential to improve early diagnosis, enhance preventive interventions, expand access to care, and support more personalized management of pediatric patients in outpatient settings. However, significant challenges remain, including limited pediatric-specific validation, algorithmic bias, data privacy concerns, regulatory uncertainties, workflow integration barriers, and the need to maintain trust among clinicians and caregivers. This narrative review summarizes current evidence regarding AI applications in ambulatory pediatrics, preventive pediatrics, and pediatric outpatient care, highlighting their clinical utility, limitations, implementation challenges, and future directions. This article is the second in a four-part series on AI in pediatrics; subsequent articles will discuss AI applications in pediatric inpatient and critical care settings, as well as the ethical, regulatory, and future implications of AI in child healthcare