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Machine Learning

The Role of Artificial Intelligence in Nutrition Research: A Scoping Review

Andrea Sosa-Holwerda(Department of Nutritional Sciences, Texas Tech University, Lubbock, TX 79409, USA), Oak-Hee Park(College of Health & Human Sciences, Texas Tech University, Lubbock, TX 79409, USA), Kembra Albracht-Schulte(Department of Kinesiology & Sport Management, Texas Tech University, Lubbock, TX 79409, USA), Surya Niraula(Department of Nutritional Sciences, Texas Tech University, Lubbock, TX 79409, USA), Leslie Thompson(Department of Animal and Food Sciences, Texas Tech University, Lubbock, TX 79409, USA), Wilna Oldewage-Theron(Department of Nutritional Sciences, Texas Tech University, Lubbock, TX 79409, USA)
June 28, 2024Nutrients78 citations
DOI

78

Citations

4

Influential Citations

Nutrients

Venue

2024

Year

Abstract

Artificial intelligence (AI) refers to computer systems doing tasks that usually need human intelligence. AI is constantly changing and is revolutionizing the healthcare field, including nutrition. This review’s purpose is four-fold: (i) to investigate AI’s role in nutrition research; (ii) to identify areas in nutrition using AI; (iii) to understand AI’s future potential impact; (iv) to investigate possible concerns about AI’s use in nutrition research. Eight databases were searched: PubMed, Web of Science, EBSCO, Agricola, Scopus, IEEE Explore, Google Scholar and Cochrane. A total of 1737 articles were retrieved, of which 22 were included in the review. Article screening phases included duplicates elimination, title-abstract selection, full-text review, and quality assessment. The key findings indicated AI’s role in nutrition is at a developmental stage, focusing mainly on dietary assessment and less on malnutrition prediction, lifestyle interventions, and diet-related diseases comprehension. Clinical research is needed to determine AI’s intervention efficacy. The ethics of AI use, a main concern, remains unresolved and needs to be considered for collateral damage prevention to certain populations. The studies’ heterogeneity in this review limited the focus on specific nutritional areas. Future research should prioritize specialized reviews in nutrition and dieting for a deeper understanding of AI’s potential in human nutrition.

Analysis

Why This Paper Matters

This scoping review is significant because it systematically consolidates the current state of AI in nutrition research, a field that is rapidly evolving but lacks comprehensive syntheses. By searching eight major databases and screening over 1,700 articles, the authors provide a rigorous overview of where AI is being applied—primarily in dietary assessment—and where gaps remain, such as in malnutrition prediction and lifestyle interventions. This is crucial for researchers and practitioners who need to understand the landscape before investing in new projects or clinical applications.

The paper also underscores the developmental stage of AI in nutrition, which is a critical insight for stakeholders. It suggests that while AI holds promise, it is not yet mature enough for widespread clinical deployment without further evidence. The emphasis on unresolved ethical concerns is particularly timely, as AI systems can inadvertently perpetuate biases or cause harm to vulnerable populations. This review serves as a call to action for the nutrition community to address these issues proactively.

Technical Contributions

  • Comprehensive search strategy: Utilized eight databases, including PubMed, Web of Science, and IEEE Explore, to ensure broad coverage of both biomedical and technical literature.
  • Structured screening process: Implemented a multi-phase screening (duplicate elimination, title-abstract, full-text, quality assessment) to ensure only relevant, high-quality studies were included.
  • Categorization of AI applications: Identified key areas of AI use in nutrition, such as dietary assessment, malnutrition prediction, and lifestyle interventions, providing a taxonomy for future research.
  • Highlighting research gaps: Clearly delineated under-explored areas, such as AI for diet-related disease comprehension, guiding future research priorities.
  • Ethical framework consideration: Brought attention to the ethical implications of AI in nutrition, a dimension often overlooked in technical reviews.

Results

The review retrieved 1,737 articles, of which 22 met inclusion criteria. The key finding is that AI's role in nutrition is at a developmental stage, with a predominant focus on dietary assessment. Other areas like malnutrition prediction and lifestyle interventions received less attention. The heterogeneity of the studies prevented a focused analysis on specific nutritional domains, limiting the depth of conclusions. The authors also noted that clinical research is needed to determine AI's intervention efficacy, and that ethical concerns remain unresolved, particularly regarding potential collateral damage to certain populations.

Significance

This review provides a foundational map for AI in nutrition, highlighting both opportunities and challenges. It underscores the need for more rigorous clinical trials to validate AI interventions and for the development of ethical guidelines to prevent harm. By identifying research gaps, it directs future efforts toward areas like malnutrition prediction and diet-related disease understanding, which could have significant public health impact. The paper also serves as a resource for interdisciplinary collaboration between AI researchers and nutrition scientists, fostering a more integrated approach to tackling global nutrition challenges.

artificial-intelligencenutritiondietary-assessmentscoping-reviewmachine-learninghealthcareethics

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