Artificial intelligence in pediatric endocrinology: clinical applications, governance, and future directions

Artificial intelligence has transitioned from theoretical promise to practical implementation across medicine including pediatric endocrinology. This review examines artificial intelligence applications across diabetes care, obesity management, thyroid disorders, growth and puberty, and bone age assessment followed by discussion of limitations and future directions.

Artificial intelligence-driven tools ranging from machine learning algorithms to multimodal large language models enable personalized risk prediction, diagnostic support, and treatment optimization. Human-in-the-Loop frameworks enhance safety by integrating computational efficiency with clinician oversight. In diabetes, artificial intelligence supports glycemic forecasting, automated insulin delivery, complication screening, and digital twin modeling for therapeutic discovery. In obesity, thyroid disease, growth disorders, and bone age assessment, artificial intelligence enhances diagnostic accuracy, risk stratification, and clinical workflow efficiency. However, widespread implementation raises concerns regarding algorithmic bias, data privacy, transparency, regulatory oversight, and health equity. Addressing these limitations through explainable artificial intelligence, federated learning, and continuous performance monitoring is essential.

Artificial intelligence should be viewed as an augmentative tool that enhances clinical judgment in pediatric endocrinology. Preparing the next generation of pediatric endocrinologists will require intentional integration of artificial intelligence literacy into training programs, equipping clinicians to critically evaluate algorithms, interpret outputs responsibly, and participate in the ethical development of future artificial intelligence-enabled tools.