Artificial Intelligence in Nutrients Science Research: A Review

Abstract

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.

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“…Several novel techniques have recently been proposed thanks to advances in Artificial Intelligence (AI) applications. AI has expanded in different domains using images with new opportunities in nutrient science research [ 17 ]. In a review, mobile applications based on systems using AI were of significant importance in the different fields of studies on biomedical and clinical nutrients research and nutritional epidemiology.…”

“…In a review, mobile applications based on systems using AI were of significant importance in the different fields of studies on biomedical and clinical nutrients research and nutritional epidemiology. Among the available AI applications, two algorithms can be used: machine learning (ML) algorithms, widely used in studies on the influence of nutrients on the functioning of the human body in health and disease; and deep learning (DL) algorithms, used in clinical studies on nutrient intake [ 17 ]. ML is an AI domain related to algorithms that improve automatically through gathered experience, making it possible to create mathematical models for decision-making.…”