ArticleFrontiers in nutrition2025
An AI-based nutrition recommendation system: technical validation with insights from Mediterranean cuisine.
Article in Frontiers in nutrition, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Molecular basis of precision nutrition: Food components, microbiome-derived metabolites, and multi-omics modeling.Food chemistry. Molecular sciences · 2026Review
- Associations between digital healthy diet literacy, artificial intelligence attitudes, mediterranean diet adherence, and health-related quality of life in adults.BMC public health · 2026Article
- An AI-driven multivariate approach for personalized healthy eating recommendations aligned with sustainable healthy diet food-group guidelines.Frontiers in nutrition · 2026Article
- Large Language Models for Real-World Nutrition Assessment: Structured Prompts, Multi-Model Validation and Expert Oversight.Nutrients · 2025Article
- SWITCHtoHEALTHY AI-Based Family Nutrition Recommendation System: Promoting the Mediterranean Diet.Nutrients · 2025Article
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Authors and funding
7 authors.
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Abstract
Introduction: Modern lifestyle trends such as sedentary behaviors and unhealthy diets pose a major health challenge, as they have been related to multiple pathologies. Following a healthy diet has become increasingly difficult in today's fast-paced world. Given this context, artificial intelligence can play a pivotal role in addressing the challenge. Methods: We present an AI-based nutrition recommendation system that generates balanced, personalized weekly meal plans tailored to the nutritional needs and preferences of healthy adults. The proposed method retrieves dishes and meals from an expert-validated database featuring Mediterranean foods, following a structured four-step process to recommend a weekly Nutrition Plan (NP). Results: The system's performance is evaluated across 4,000 generated user profiles in three key areas: (a) dish/meal filtering accuracy based on user-specific parameters (e.g., allergies), (b) diversity of meals and food group balance, and (c) accuracy in caloric and macronutrient recommendations. The system achieves high accuracy in terms of suggested caloric and nutrient content while ensuring seasonality, diversity, and food group variety. Discussion: With solid accuracy in filtering, diversity, and caloric/macronutrient suggestions, the proposed system offers a promising solution to modern dietary challenges.
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