Enterprise AI Analysis
A Scoping Review of Artificial Intelligence for Precision Nutrition
This deep-dive analysis by OwnYourAI leverages advanced AI to distill key insights, potential applications, and strategic recommendations from cutting-edge research.
Executive Impact at a Glance
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=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.
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Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Explores the current state of AI applications in precision nutrition, including publication trends, targeted disease areas, and the evolution of AI methods.
Details the AI methods employed, datasets used, and evaluation metrics for assessing model performance and health outcomes.
Addresses the importance of minority and cultural factors, ethical considerations, and identifies gaps and future research opportunities.
Recent Research Surge
0 of AI-driven precision nutrition research published since 2020The field has seen rapid growth, with a significant majority of studies appearing in the last few years, indicating increasing interest and technological advancement.
Scoping Review Methodology
Article Retrieval (881 duplicates removed)
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Abstract Screening (881 articles)
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Full-Text Screening (427 articles)
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Animal Studies Exclusion (49 articles)
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Final Included Articles (198)
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AI for Diabetes Self-Management
Bul et al. (2023) evaluated a web-based AI-driven nutrition platform designed to assist people with diabetes and their caregivers. It provided personalized recipe recommendations, meal planning, and online shopping.
Key Takeaway: AI platforms can significantly improve diabetes self-management by offering personalized, actionable dietary guidance.
Top Targeted Disease Area
Diabetes most frequently studied disease in AI precision nutrition (67 articles)Diabetes consistently attracts the most research attention, with AI approaches aimed at self-management, prevention, and treatment.
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AI Model Application Process
Patient Data Collection (Multiomic, dietary, medical history)
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AI Model Training (Identify patterns, predict responses)
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Personalized Recommendations (Dietary, lifestyle)
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Health Optimization / Disease Management
Top AI Method Usage
0 studies used Conventional AI methodsConventional AI methods, including regression, clustering, and feature extraction, are the most commonly applied due to their well-defined procedures.
Addressing Bias in AI Precision Nutrition
Figueroa et al. (2021) designed chatbots in Spanish to address linguistic bias common in English-centric health chatbots. Other studies integrate demographic and cultural variables (gender, ethnicity, education) to mitigate potential biases.
Key Takeaway: Cultural sensitivity and demographic consideration are crucial for equitable AI application in diverse populations.
Emerging AI Gap
0 studies utilized Generative AI (GenAI)Despite GenAI's groundbreaking potential in data generation and personalized recommendations, its application in precision nutrition is still limited.
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