Emerging role of artificial intelligence in necrotizing enterocolitis and implementation challenges

Necrotizing enterocolitis (NEC) remains a persistent clinical challenge, with diagnostic strategies largely relying on reactive staging criteria that have not evolved significantly in decades. This commentary synthesizes emerging literature to evaluate the transformative potential of Artificial Intelligence (AI) and Machine Learning (ML) in shifting NEC management toward predictive precision. We review how ML algorithms are redefining risk stratification by integrating multimodal data. AI demonstrates superior utility in automating radiographic diagnosis, analyzing complex data to identify early disease progression, distinguishing medical from surgical phenotypes and providing objective support for difficult intervention decisions. Despite this promise, clinical translation is currently limited by data heterogeneity, small sample sizes, and the black box nature of complex algorithms. Further, the integration of AI methodologies into the clinical IT ecosystem requires careful planning, to assess how such tools affect clinical workflows, how performance changes over time, when they need to be retrained, changed, or shelved, e.g., ML operations (MLOps). We highlight that realizing AI's potential in the NICU requires a paradigm shift toward multicenter data sharing, the development of explainable AI models, and a rigorous ethical framework to ensure these tools augment rather than obscure clinical judgment. IMPACT: Traditional reliance on Bell's staging for NEC diagnosis is reactive and lacks the specificity required for early intervention. Artificial Intelligence and Machine Learning offer a transformative approach, demonstrating superior accuracy in risk stratification and surgical prediction by synthesizing complex, multimodal data that elude conventional clinical assessment. Future translational research must prioritize multicenter validation and algorithmic interpretability to safely integrate these predictive tools into real-time neonatal care.