Artificial Intelligence in Nutrients Science Research: A Review.
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 i...
| Publicado en: | Nutrients Vol. 13; no. 2; pp. 322 - 323 |
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| Autores principales: | , |
| Formato: | research systematic review tables/charts Journal Article |
| Publicado: |
MDPI
Feb2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=149044442&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149044442 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726643 B0TT jtl: Nutrients issn: 20726643 maglogo: N pubinfo: dt: Feb2021 vid: 13 iid: 2 pid: 97109 pub: MDPI artinfo: ui: 149044442 149044442 149044442 10.3390/nu13020322 149044442 ppf: 322 ppct: 1 formats: tig: atl: Artificial Intelligence in Nutrients Science Research: A Review. aug: au: Sak, Jarosław Suchodolska, Magdalena affil: Chair and Department of Humanities and Social Medicine, Medical University of Lublin, 20-093 Lublin, Poland sug: subj: Artificial Intelligence Utilization Nutrients Research, Medical Human Systematic Review PubMed Machine Learning Algorithms Deep Learning ab: 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. pubtype: Academic Journal doctype: research systematic review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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