Emerging role of artificial intelligence in necrotizing enterocolitis and implementation challenges.

Area of Science:

• Neonatal Medicine
• Artificial Intelligence in Healthcare
• Medical Informatics

Background:

• Necrotizing enterocolitis (NEC) presents a significant clinical challenge, with current diagnostic criteria being reactive and outdated.
• Existing diagnostic strategies for NEC lack the specificity needed for timely and effective intervention.

Purpose of the Study:

• To evaluate the potential of Artificial Intelligence (AI) and Machine Learning (ML) to transition NEC management towards predictive precision.
• To explore how AI/ML can enhance risk stratification, radiographic diagnosis, and intervention decisions in NEC.

Main Methods:

• Synthesis of emerging literature on AI and ML applications in NEC management.
• Review of ML algorithms for risk stratification using multimodal data.
• Analysis of AI's utility in radiographic diagnosis and distinguishing NEC phenotypes.

Main Results:

• AI/ML algorithms show superior accuracy in risk stratification and predicting surgical NEC.
• AI demonstrates enhanced capabilities in automated radiographic diagnosis and early disease detection.
• AI tools can provide objective support for critical intervention decisions in NEC cases.

Conclusions:

• AI and ML offer a transformative approach to NEC diagnosis, moving beyond reactive staging to predictive precision.
• Clinical translation requires addressing data heterogeneity, sample size limitations, and the interpretability of AI models.
• Future research must focus on multicenter validation, explainable AI, and ethical frameworks for successful NICU integration.