A Scoping Review of Artificial Intelligence for Precision Nutrition.

Journal: Advances in nutrition (Bethesda, Md.)
PMID: 40024275

Abstract

With the role of artificial intelligence (AI) in precision nutrition rapidly expanding, a scoping review on recent studies and potential future directions is needed. This scoping review examines: 1) the current landscape, including publication venues, targeted diseases, AI applications, methods, evaluation metrics, and considerations of minority and cultural factors; 2) common patterns in AI-driven precision nutrition studies; and 3) gaps, challenges, and future research directions. Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) process, we extracted 198 articles from major databases using search keywords in 3 categories: precision nutrition, AI, and natural language processing. The extracted literature reveals a surge in AI-driven precision nutrition research, with ∼75% (n = 148) published since 2020. It also showcases a diverse publication landscape, with the majority of studies focusing on diet-related diseases, such as diabetes and cardiovascular conditions, while emphasizing health optimization, disease prevention, and management. We highlight diverse datasets used in the literature and summarize methodologies and evaluation metrics to guide future studies. We also emphasize the importance of minority and cultural perspectives in promoting equity for precision nutrition using AI. Future research should further integrate these factors to fully harness AI's potential in precision nutrition.

Authors

  • Xizhi Wu
    Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA, United States.
  • David Oniani
    Kern Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, Minnesota, USA.
  • Zejia Shao
    Siebel School of Computing and Data Science, The Grainger College of Engineering, University of Illinois Urbana-Champaign, Champaign, IL, United States.
  • Paul Arciero
    Department of Health and Human Physiological Sciences, Skidmore College, Saratoga Springs, NY, United States.
  • Sonish Sivarajkumar
    Intelligent Systems Program, School of Computing and Information, University of Pittsburgh, PA.
  • Jordan Hilsman
    Department of Health Information Management, University of Pittsburgh, Pittsburgh, PA, USA.
  • Alex E Mohr
    College of Health Solutions, Arizona State University, Phoenix, AZ, United States.
  • Stephanie Ibe
    School of Medicine, Stanford University, Stanford, CA, United States.
  • Minal Moharir
    School of Medicine, Stanford University, Stanford, CA, United States.
  • Li-Jia Li
    Stanford School of Medicine, Stanford, USA.
  • Ramesh Jain
    University of California, Irvine, Irvine, California, United States of America.
  • Jun Chen
    Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA 90095, USA.
  • Yanshan Wang
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.

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