ReviewCurrent opinion in clinical nutrition and metabolic care2026
Artificial intelligence-guided nutritional therapy in the ICU.
Review in Current opinion in clinical nutrition and metabolic care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Artificial Intelligence-Guided Artificial Nutrition in Critical Illness: Integrating Indirect Calorimetry and BIVA for Metabolic Precision.Nutrients · 2026Review
- Artificial Intelligence in Parenteral Nutrition: Enhancing Patient Outcomes Through Global Experience and the Bulgarian Context.Nutrients · 2026Review
- Development and validation of an explainable machine learning model for mortality prediction in ICU patients with lung cancer.Frontiers in cell and developmental biology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
purpose of reviewCritical 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
findingsWidely 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.
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Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.