A Scoping Review of Artificial Intelligence for Precision Nutrition

Abstract

ABSTRACT With the role of artificial intelligence (AI) in precision nutrition rapidly expanding, a scoping review on recent studies and potential future directions is needed. This scoping review examines: 1) the current landscape, including publication venues, targeted diseases, AI applications, methods, evaluation metrics, and considerations of minority and cultural factors; 2) common patterns in AI-driven precision nutrition studies; and 3) gaps, challenges, and future research directions. Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) process, we extracted 198 articles from major databases using search keywords in 3 categories: precision nutrition, AI, and natural language processing. The extracted literature reveals a surge in AI-driven precision nutrition research, with ~75% (n ¼ 148) published since 2020. It also showcases a diverse publication landscape, with the majority of studies focusing on diet-related diseases, such as diabetes and cardiovascular conditions, while emphasizing health optimization, disease prevention, and management. We highlight diverse datasets used in the literature and summarize methodologies and evaluation metrics to guide future studies. We also emphasize the importance of minority and cultural perspectives in promoting equity for precision nutrition using AI. Future research should further integrate these factors to fully harness AI’s potential in precision nutrition. Keywords: artificial Intelligence, precision nutrition, machine learning, deep learning, literature review