Advances in Artificial Intelligence and Machine Learning for Precision Medicine in Necrotizing Enterocolitis and Neonatal Sepsis: A State-of-the-Art Review

Abstract

Necrotizing enterocolitis remains one of the most severe gastrointestinal diseases in neonates, particularly affecting preterm infants. It is characterized by intestinal inflammation and necrosis, with significant morbidity and mortality despite advancements in neonatal care. Recent advancements in artificial intelligence (AI) and machine learning (ML) have shown potential in improving NEC prediction, early diagnosis, and management. A systematic search was conducted across multiple databases to explore the application of AI and ML in predicting NEC risk, diagnosing the condition at early stages, and optimizing treatment strategies.AI-based models demonstrated enhanced accuracy in NEC risk stratification compared to traditional clinical approaches. Machine learning algorithms identified novel biomarkers associated with disease onset and severity. Additionally, deep learning applied to medical imaging improved NEC diagnosis by detecting abnormalities earlier than conventional methods. The integration of AI and ML in NEC research provides promising insights into patient-specific risk assessment. However, challenges such as data heterogeneity, model interpretability, and the need for large-scale validation studies remain. Future research should focus on translating AI-driven findings into clinical practice, ensuring ethical considerations and regulatory compliance.

Keywords: NEC; artificial intelligence; machine learning; precision medicine.