Abstract
Application of artificial intelligence (AI) within electronic health record (EHR) systems is influencing clinical care, research, education, and quality improvement in pediatric surgical practice. In clinical settings, these technologies organize and structure patient information, enable predictive analytics, and support decision-making tools that enhance awareness and facilitate interventions.Longitudinal datasets in EHR create opportunities for data-driven research ranging from modeling and outcome prediction to clinical trial recruitment. Educational applications of AI-enabled EHR extend to healthcare professionals, trainees, patients, and caregivers. For professionals, case-based learning and simulation resources strengthen decision-making skills and clinical judgment. For patients and caregivers, personalized guidance and accessible health records foster understanding of disease, encourage active participation in care, and improve adherence to management plans. Quality improvement initiatives benefit from AI-assisted audit systems, automated tracking of outcomes, and identification of workflow inefficiencies. These benefits are accompanied by challenges, including the risk of overreliance on automated alerts, limited representativeness of training data, lack of external validation, workflow misalignment, and concerns about privacy and transparency. Addressing these issues through rigorous validation, inclusive datasets, user-focused design, and informatics education will be essential to ensure that AI-enabled EHR systems enhance rather than replace clinician expertise, while promoting safe, equitable, and patient-centered care.