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. 2024 Apr 6;16(7):1073.
doi: 10.3390/nu16071073.

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

Tagne Poupi Theodore Armand  1 , Kintoh Allen Nfor  2 , Jung-In Kim  1 , Hee-Cheol Kim  1   2   3
Affiliations

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

Tagne Poupi Theodore Armand et al. Nutrients. .

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.

Keywords: artificial intelligence; deep learning; diet; machine learning; nutrition.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The literature selection process.
Figure 2
Figure 2
Validated studies are included in the systematic review.
Figure 3
Figure 3
Evolution of relevant research studies in 2019–2024.
Figure 4
Figure 4
Overview of identified research collaboration.
Figure 5
Figure 5
Collaboration patterns between engineering and nutrition/related authors.
Figure 6
Figure 6
Keyword analysis.
Figure 7
Figure 7
Proposed conceptual framework for AI, ML, and DL applications in nutrition.
See this image and copyright information in PMC

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