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Review

Artificial Intelligence in Nutrients Science Research: A Review

1
Chair and Department of Humanities and Social Medicine, Medical University of Lublin, 20-093 Lublin, Poland
2
BioMolecular Resources Research Infrastructure Poland (BBMRI.pl), Poland
3
Faculty of Medicine, Medical University of Lublin, 20-059 Lublin, Poland
*
Author to whom correspondence should be addressed.
Nutrients 2021, 13(2), 322; https://doi.org/10.3390/nu13020322
Received: 16 December 2020 / Revised: 12 January 2021 / Accepted: 18 January 2021 / Published: 22 January 2021
(This article belongs to the Section Nutrition Methodology & Assessment)
Artificial intelligence (AI) as a branch of computer science, the purpose of which is to imitate thought processes, learning abilities and knowledge management, finds more and more applications in experimental and clinical medicine. In recent decades, there has been an expansion of AI applications in biomedical sciences. The possibilities of artificial intelligence in the field of medical diagnostics, risk prediction and support of therapeutic techniques are growing rapidly. The aim of the article is to analyze the current use of AI in nutrients science research. The literature review was conducted in PubMed. A total of 399 records published between 1987 and 2020 were obtained, of which, after analyzing the titles and abstracts, 261 were rejected. In the next stages, the remaining records were analyzed using the full-text versions and, finally, 55 papers were selected. These papers were divided into three areas: AI in biomedical nutrients research (20 studies), AI in clinical nutrients research (22 studies) and AI in nutritional epidemiology (13 studies). It was found that the artificial neural network (ANN) methodology was dominant in the group of research on food composition study and production of nutrients. However, machine learning (ML) algorithms were widely used in studies on the influence of nutrients on the functioning of the human body in health and disease and in studies on the gut microbiota. Deep learning (DL) algorithms prevailed in a group of research works on clinical nutrients intake. The development of dietary systems using AI technology may lead to the creation of a global network that will be able to both actively support and monitor the personalized supply of nutrients. View Full-Text
Keywords: artificial intelligence; artificial neural networks; machine learning; nutrients artificial intelligence; artificial neural networks; machine learning; nutrients
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MDPI and ACS Style

Sak, J.; Suchodolska, M. Artificial Intelligence in Nutrients Science Research: A Review. Nutrients 2021, 13, 322. https://doi.org/10.3390/nu13020322

AMA Style

Sak J, Suchodolska M. Artificial Intelligence in Nutrients Science Research: A Review. Nutrients. 2021; 13(2):322. https://doi.org/10.3390/nu13020322

Chicago/Turabian Style

Sak, Jarosław; Suchodolska, Magdalena. 2021. "Artificial Intelligence in Nutrients Science Research: A Review" Nutrients 13, no. 2: 322. https://doi.org/10.3390/nu13020322

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