Artificial intelligence in nutrition research: perspectives on current and future applications

新颖性 数据科学 领域(数学) 计算机科学 人工智能 人工智能应用 大数据 管理科学 心理学 工程类 数据挖掘 数学 社会心理学 纯数学
作者
Mélina Côté,Benoı̂t Lamarche
出处
期刊:Applied Physiology, Nutrition, and Metabolism [Canadian Science Publishing]
卷期号:47 (1): 1-8 被引量:31
标识
DOI:10.1139/apnm-2021-0448
摘要

Artificial intelligence (AI) is a rapidly evolving area that offers unparalleled opportunities of progress and applications in many healthcare fields. In this review, we provide an overview of the main and latest applications of AI in nutrition research and identify gaps to address to potentialize this emerging field. AI algorithms may help better understand and predict the complex and non-linear interactions between nutrition-related data and health outcomes, particularly when large amounts of data need to be structured and integrated, such as in metabolomics. AI-based approaches, including image recognition, may also improve dietary assessment by maximizing efficiency and addressing systematic and random errors associated with self-reported measurements of dietary intakes. Finally, AI applications can extract, structure and analyze large amounts of data from social media platforms to better understand dietary behaviours and perceptions among the population. In summary, AI-based approaches will likely improve and advance nutrition research as well as help explore new applications. However, further research is needed to identify areas where AI does deliver added value compared with traditional approaches, and other areas where AI is simply not likely to advance the field. Novelty: Artificial intelligence offers unparalleled opportunities of progress and applications in nutrition. There remain gaps to address to potentialize this emerging field.
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