A Scoping Review of Artificial Intelligence for Precision Nutrition

Area of Science:

• Nutritional Science
• Biomedical Informatics
• Artificial Intelligence

Background:

• The application of artificial intelligence (AI) in precision nutrition is rapidly growing.
• A comprehensive understanding of the current research landscape and future directions is essential.

Purpose of the Study:

• To conduct a scoping review of AI applications in precision nutrition.
• To identify publication trends, targeted diseases, methodologies, and evaluation metrics.
• To explore the integration of minority and cultural factors and highlight research gaps.

Main Methods:

• A scoping review following the PRISMA-ScR guidelines.
• Extraction of 198 articles from major databases using keywords related to precision nutrition, AI, and natural language processing.
• Analysis of publication venues, targeted diseases, AI applications, methods, evaluation metrics, and cultural considerations.

Main Results:

• A significant increase in AI-driven precision nutrition research, with 75% published since 2020.
• Focus on diet-related diseases like diabetes and cardiovascular conditions, emphasizing health optimization and disease management.
• Diverse datasets, methodologies, and evaluation metrics identified, with a call for integrating minority and cultural perspectives.

Conclusions:

• AI is increasingly utilized in precision nutrition, particularly for common diet-related diseases.
• Future research needs to prioritize the inclusion of diverse populations and cultural contexts to ensure equitable AI applications in nutrition.
• Further integration of these factors is crucial to fully realize AI's potential in personalized nutrition strategies.