Artificial intelligence-guided nutritional therapy in the ICU

Purpose of review Critical care nutrition remains a high-stakes and error-prone domain, particularly given the complex metabolic demands and heterogeneity of ICU populations. This review explores recent progress in integrating artificial intelligence with nutritional therapy in ICUs, highlighting its evolution and potential benefits in precision-guided support, along with current implementation challenges. Recent findings Widely used in adult and neonatal ICUs, parenteral nutrition faces persistent challenges including ordering errors, practice variability, and insufficient robust long-term outcome evidence. Recent advances in machine learning have demonstrated considerable potential in predicting nutrition-related complications (e.g. neonatal morbidities, cholestasis, feeding intolerances, and malnutrition), optimizing nutrient delivery through dynamic, real-time recommendations, and enhancing clinical decision-making with large language models (LLMs) that synthesize clinical guidelines and patient data into actionable insights. However, future studies must establish causal relationships between optimal parenteral nutrition and long-term outcomes while addressing confounding factors and ingredient heterogeneity. Summary Artificial intelligence-driven nutrition therapies have the potential to significantly improve the precision, safety, and personalization of ICU nutrition practices. Continued development and validation using standardized, comprehensive, longitudinal datasets, and validation in comparative clinical trials will be critical to realizing this transformative potential.