Artificial intelligence in nutritional assessment and decision making.

IF 3.8 3区医学 Q2 ENDOCRINOLOGY & METABOLISM

Current Opinion in Clinical Nutrition and Metabolic Care Pub Date : 2026-09-01 Epub Date: 2026-05-04 DOI:10.1097/MCO.0000000000001229

Pierre Singer, Michal Slavin Kish, Orit Raphaeli

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引用次数: 0

Abstract

Purpose of the review: Artificial intelligence (AI) has become an non contourable tool in clinical nutrition practice. This review proposes to discuss the most recent advances that can support the physician in nutritional assessment and mainly physician decision support systems trying to predict and prevent nutritional related complications.

Recent findings: Advanced data storage systems improve screening and assessment tools using large database analysis. Medical CT images analysis can determine patients suffering from sarcopenia and suggest outcome predictions accordingly. Decision making of the type of parenteral nutrition formula according to cluster obtained by machine of large databases has been shown to be superior to the prescription of neonatologists in preterm children. Machine learning can help to anticipate enteral feeding intolerance and predict enteral nutrition feeding success. Numerous digital technologies support analysis of the meal, allows for the passive monitoring of eating behaviors, including acoustic sensors for swallowing detection and motion sensors for tracking hand-to-mouth gestures.

Summary: Each step in clinical nutrition, from screening to clinical decision-making, can be improved using AI. However, large data sources, the participation of data scientists, and advanced technologies are required. These improvements have the potential to transform clinical nutrition toward personalized nutrition, but AI integration should be carefully monitored to ensure patient benefit and safety.

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人工智能在营养评估和决策中的应用。

综述目的：人工智能（AI）已成为临床营养实践中不可避免的工具。这篇综述旨在讨论支持医生进行营养评估的最新进展，主要是医生决策支持系统试图预测和预防营养相关并发症。最新发现：先进的数据存储系统使用大型数据库分析改进了筛选和评估工具。医学CT图像分析可以确定患有肌肉减少症的患者，并据此提出预后预测。根据大型数据库机器获得的聚类对早产儿进行肠外营养配方类型的决策优于新生儿医生的处方。机器学习可以帮助预测肠内喂养不耐受和预测肠内营养喂养成功。许多数字技术支持对食物的分析，允许被动监测饮食行为，包括用于吞咽检测的声学传感器和用于跟踪手到嘴手势的运动传感器。总结：临床营养从筛选到临床决策的每一步都可以通过人工智能进行改进。但是，需要大量的数据源、数据科学家的参与和先进的技术。这些改进有可能将临床营养转变为个性化营养，但应仔细监测人工智能集成，以确保患者受益和安全。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Current Opinion in Clinical Nutrition and Metabolic Care 医学-内分泌学与代谢

CiteScore

5.30

自引率

6.50%

发文量

116

审稿时长

6-12 weeks

期刊介绍： A high impact review journal which boasts an international readership, Current Opinion in Clinical Nutrition and Metabolic Care offers a broad-based perspective on the most recent and exciting developments within the field of clinical nutrition and metabolic care. Published bimonthly, each issue features insightful editorials and high quality invited reviews covering two or three key disciplines which include protein, amino acid metabolism and therapy, lipid metabolism and therapy, nutrition and the intensive care unit and carbohydrates. Each discipline introduces world renowned guest editors to ensure the journal is at the forefront of knowledge development and delivers balanced, expert assessments of advances from the previous year.