Application of AI in neonatal gastroenterology and nutrition

Optimizing neonatal nutrition and diagnosing serious gastrointestinal diseases remains a challenge, as traditional guideline-based approaches often fail to address the individualized needs of preterm and term infants. Advances in artificial intelligence and machine learning provide opportunities for precision diagnostics and therapeutics by incorporating multiomic data and clustering infants based on risk factors and metabolic profiles. For example, machine learning is redefining necrotizing enterocolitis as a spectrum of intestinal injuries rather than a single disease, while digital twin models offer the potential for real-time personalized nutrition optimization. Moreover, integration of advanced gastrointestinal monitoring methods using novel biomarkers and sensor technologies may further enhance early detection and intervention strategies. Altogether, these digital technological advancements may lead to identification of early predictors of nutritional deficiencies and prompt recognition of gastrointestinal pathologies, thereby allowing for proactive interventions and potentially improved outcomes in the neonatal population.