Abstract
Necrotizing enterocolitis (NEC) is a life-threatening gastrointestinal disease of neonates with a multifactorial pathogenesis involving prematurity, low birth weight, hypoxia, infection, and immune dysregulation. Owing to its superior data processing and diagnostic capabilities, artificial intelligence (AI) has been increasingly applied to support clinical care. By analyzing clinical and imaging data, AI approaches can aid in early identification, differential diagnosis, treatment decision-making, and prognostic evaluation, thereby complementing clinician judgment. This review summarizes recent advances in the application of AI and machine learning for NEC diagnosis and management, comparing the characteristics and strengths of different algorithms. The aim is to provide a reference for further development and implementation of AI-assisted tools in this field.