AI Applications for Nutrition and Food Security Research

Data analytics (DA) and artificial intelligence (AI) play increasingly vital roles in food, nutrition, environment, and public health research and practice. With the continued successful development, professionals in these fields could rely on data analytics and AI for tasks such as data collection, decision-making, and policy development. Proficiency in research methods, statistical analysis, and ethical considerations is crucial. As AI applications grow in complexity, it is essential for professionals and the public to embrace and regulate them effectively. This review outlines AI’s diverse applications in nutrition research, including data analysis, prediction, personalized recommendations, and food safety monitoring. Select illustrative examples demonstrate its potential across various domains and highlight common challenges. The narrative underscores the importance of integrating data science and AI competencies into graduate education to equip the modern workforce.

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Publication Details

Journal
Nutrition Today
Published
2024-10-14
DOI
https://doi.org/10.1097/nt.0000000000000708
Citations
5
Primary Topic
Nutritional Studies and Diet
Type
article
Field-Weighted Citation Impact
2.71
Controls
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article

AI Applications for Nutrition and Food Security Research

Elena N. Naumova
5 citations
Nutrition Today
Nutritional Studies and Diet
2.71
article

AI Applications for Nutrition and Food Security Research

Elena N. Naumova
article en
5 citations

Abstract

Data analytics (DA) and artificial intelligence (AI) play increasingly vital roles in food, nutrition, environment, and public health research and practice. With the continued successful development, professionals in these fields could rely on data analytics and AI for tasks such as data collection, decision-making, and policy development. Proficiency in research methods, statistical analysis, and ethical considerations is crucial. As AI applications grow in complexity, it is essential for professionals and the public to embrace and regulate them effectively. This review outlines AI’s diverse applications in nutrition research, including data analysis, prediction, personalized recommendations, and food safety monitoring. Select illustrative examples demonstrate its potential across various domains and highlight common challenges. The narrative underscores the importance of integrating data science and AI competencies into graduate education to equip the modern workforce.

Nutrition TodayVol. 60(1)
New England Aquarium (US)
Openalex Percentile: Top 9%
Nutritional Studies and Diet
2.71
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