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Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review

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Theodore Armand, Tagne Poupi, Nfor, Kintoh Allen, Kim, Jung-In, Kim, Hee-Cheol·2024·Nutrients

DOI 10.3390/nu16071073PMC PMC11013624

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Abstract

In industry 4.0, where the automation and digitalization of entities and processes are fundamental, artificial intelligence (AI) is increasingly becoming a pivotal tool offering innovative solutions in various domains. In this context, nutrition, a critical aspect of public health, is no exception to the fields influenced by the integration of AI technology. This study aims to comprehensively investigate the current landscape of AI in nutrition, providing a deep understanding of the potential of AI, machine learning (ML), and deep learning (DL) in nutrition sciences and highlighting eventual challenges and futuristic directions. A hybrid approach from the systematic literature review (SLR) guidelines and the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines was adopted to systematically analyze the scientific literature from a search of major databases on artificial intelligence in nutrition sciences. A rigorous study selection was conducted using the most appropriate eligibility criteria, followed by a methodological quality assessment ensuring the robustness of the included studies. This review identifies several AI applications in nutrition, spanning smart and personalized nutrition, dietary assessment, food recognition and tracking, predictive modeling for disease prevention, and disease diagnosis and monitoring. The selected studies demonstrated the versatility of machine learning and deep learning techniques in handling complex relationships within nutritional datasets. This study provides a comprehensive overview of the current state of AI applications in nutrition sciences and identifies challenges and opportunities. With the rapid advancement in AI, its integration into nutrition holds significant promise to enhance individual nutritional outcomes and optimize dietary recommendations. Researchers, policymakers, and healthcare professionals can utilize this research to design future projects and support evidence-based decision-making in AI for nutrition and dietary guidance.

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Authors: Theodore Armand, T. P., Nfor, K. A., Kim, J.-I., & Kim, H.-C.
Affiliations: Institute of Digital Anti-Aging Healthcare, Inje University, Gimhae 50834, Republic of Korea
Inje University (KR)
Corresponding author: Kim, Hee-Cheol
ORCID: Theodore Armand, Tagne Poupi
Nfor, Kintoh Allen
Kim, Jung-In
Kim, Hee-Cheol
Venue: Nutrients
Publisher: MDPI AG
Published: 2024-04-06
Volume: 16
Issue: 7
Pages: 1073
ISSN: 2072-6643 (ISSN-L)
DOI: 10.3390/nu16071073
PMC: PMC11013624
OpenAlex: W4394576240
Cited by: 253
References: 52
Citations by year: 2026: 101 · 2025: 128 · 2024: 24
FWCI: 5.71
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Nutrients (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Funders: Ministry of Science ICT (2022-0-01091, 1711175863)
Institute of Information and Communications Technology Planning and Evaluation (2022-0-01091, 1711175863)
Topics: Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Nutrition, Genetics, and Disease
Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords: artificial intelligence in nutrition, machine learning, deep learning, personalized nutrition, dietary assessment, food recognition, dietary monitoring, predictive modeling, disease prevention, disease diagnosis, disease surveillance, dietary recommendations, Industry 4.0
MeSH terms: Machine Learning, Deep Learning, Artificial Intelligence, Humans, Nutritional Status, Nutritional Sciences

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Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review

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