The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Nutr Clin Pract. 2024 Oct;39(5):1069-1080. doi: 10.1002/ncp.11194. Epub 2024 Jul 28.
Nutrition plays a key role in the comprehensive care of critically ill patients. Determining optimal nutrition strategy, however, remains a subject of intense debate. Artificial intelligence (AI) applications are becoming increasingly common in medicine, and specifically in critical care, driven by the data-rich environment of intensive care units. In this review, we will examine the evidence regarding the application of AI in critical care nutrition. As of now, the use of AI in critical care nutrition is relatively limited, with its primary emphasis on malnutrition screening and tolerance of enteral nutrition. Despite the current scarcity of evidence, the potential for AI for more personalized nutrition management for critically ill patients is substantial. This stems from the ability of AI to integrate multiple data streams reflecting patients' changing needs while addressing inherent heterogeneity. The application of AI in critical care nutrition holds promise for optimizing patient outcomes through tailored and adaptive nutrition interventions. A successful implementation of AI, however, necessitates a multidisciplinary approach, coupled with careful consideration of challenges related to data management, financial aspects, and patient privacy.
营养在危重症患者的综合治疗中起着关键作用。然而,确定最佳营养策略仍然是一个激烈争论的话题。人工智能(AI)应用在医学领域越来越普遍,特别是在重症监护中,这是由重症监护病房的数据丰富环境所驱动的。在这篇综述中,我们将检查关于 AI 在重症监护营养中的应用的证据。到目前为止,AI 在重症监护营养中的应用相对有限,主要集中在营养不良筛查和肠内营养耐受上。尽管目前证据有限,但 AI 为危重症患者提供更个性化的营养管理的潜力是巨大的。这源于 AI 整合反映患者不断变化需求的多个数据流的能力,同时解决固有异质性。AI 在重症监护营养中的应用有望通过量身定制和适应性的营养干预来优化患者的结局。然而,要成功实施 AI,需要多学科的方法,并仔细考虑与数据管理、财务方面和患者隐私相关的挑战。