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Role of artificial intelligence in critical care nutrition support and research.

Author information

Affiliations

  • 1. The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
      Authors
    • Kittrell HD 1
    • Nadkarni GN 1
    • Sakhuja A 1, 2
    (3 authors)
  • 2. Institute for Critical Care Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
      Authors
    • Shaikh A 2
    • Kohli-Seth R 2
    • Sakhuja A 1, 2
    (3 authors)
  • 3. Food and Nutrition Services Department, Memorial Sloan Kettering Cancer Center, New York, New York, USA.
      Authors
    • Adintori PA 3
    (1 author)
  • 4. Department of Cardiovascular and Thoracic Surgery, Division of Cardiovascular Critical Care, West Virginia University, Morgantown, West Virginia, USA.
      Authors
    • McCarthy P 4
    (1 author)

ORCIDs linked to this article

Nutrition in Clinical Practice : Official Publication of the American Society for Parenteral and Enteral Nutrition, 28 Jul 2024, 39(5):1069-1080
https://doi.org/10.1002/ncp.11194 PMID: 39073166 

Review

Abstract 

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.

Full text links 

Read article at publisher's site: https://doi.org/10.1002/ncp.11194

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Funding 

Funders who supported this work.

NCI NIH HHS (1)

This study was supported by National Institute of Diabetes and Digestive and Kidney Diseases (2)

  • Grant ID: 5K08DK131286 (Shaikh) and R01DK127139 (Nadkarni)

    1 publication

  • Grant ID: 5K08DK131286 (Sakhuja) and R01DK127139 (Nadkarni)

    1 publication