Artificial intelligence-supported total parenteral nutrition management in neonatal intensive care units: A systematic review of clinical efficacy, safety, and system integration.

Abstract

This systematic review aims to synthesise the present evidence base concerning artificial intelligence (AI)-supported total parenteral nutrition (TPN) management in neonatal intensive care units (NICUs) with respect to clinical efficacy, patient safety, and system integration. This systematic review follows the PRISMA 2020 guideline. We searched PubMed/MEDLINE, Scopus, and Web of Science between January and March 2026, and used the PICO framework to include quantitative studies that employed AI, machine learning (ML), computerised physician order entry (CPOE), or clinical decision support systems (CDSS) in neonatal TPN management. We appraised methodological quality using the Cochrane RoB 2 tool for randomised controlled trials, the Newcastle-Ottawa Scale for observational studies, and the GRADE framework for the overall strength of the evidence. Sixteen records met the broad topical and technological inclusion criteria. Of these, thirteen quantitative primary studies (published 2008-2026, n = 30-9,330) form the evidence base for data synthesis and GRADE appraisal; three additional records provided historical and architectural context only. CPOE and rule-based CDSS significantly improved macronutrient target attainment, glycemic control, and medication safety. The TPN2.0 transformer model attained a Pearson R = 0.94 correlation with expert decisions, whereas classical ML algorithms achieved R² > 0.70 in macronutrient prediction. CPOE implementation reduced the PN medication error rate from 10.8% to 3.2%. By contrast, only one-third of U.S. NICUs employed a CDSS. GRADE evidence was moderate for clinical efficacy and patient safety, and low for system integration. AI-supported TPN management is associated with favourable outcomes in NICUs with respect to clinical efficacy and patient safety, although this conclusion rests on only thirteen quantitative primary studies of predominantly moderate-to-low certainty and should be interpreted accordingly. The field is moving from CPOE-based automation towards deep learning models. We propose three priority areas for future research: multicentre randomised controlled trials measuring long-term neurodevelopmental outcomes, standardised TPN data repositories, and explainable AI design.