Application of Artificial Intelligence in Food Science and Nutrition: Challenges and Future Perspectives

Abstract

The Artificial intelligence (AI) and Machine Learning (ML) advent has proclaimed unparalleled knowledge transformation across several sectors, including food science and nutrition. This advancement can aid in a deeper understanding of the insights of both fields and their subfields. A systematic search was conducted across literature resources in many databases, including Web of Science, Scopus, and PubMed, according to PRISMA guidelines. The possible eligible study data were retrieved to assess eligibility and inclusion criteria. This research comprehensively explores the use AI applications in food science, such as the food industry and processing, food safety and packaging, and nutrition, including food and nutrient intake, supplements, clinical nutrition, gut microbiota, and trace elements intake. AI applications can be very helpful in addressing various issues, developing novel techniques in food production, food safety, and quality, and aiding in planning nutrition and nutrient intake for better health with high accuracy and precision. Despite these advancements in the application of AI and ML in both food science and nutrition, more improvement is needed for more efficient, precise, and accurate application in some fields.