Experimental results demonstrate that the proposed PLAN'EAT Nutrition Advisor can efficiently generate personalized, nutritionally compliant, and diverse weekly meal plans while maintaining transparent expert-rule-driven optimization.
Abstract
Translating nutritional recommendations into practical day-to-day meal choices remains a challenging task, particularly when personalization, nutritional adequacy, dietary diversity, allergies, seasonal availability, and food-group constraints must be simultaneously satisfied. This study presents and evaluates the PLAN'EAT Nutrition Advisor, an Artificial Intelligence (AI)-driven, expert rule-based nutrition recommendation system, designed to generate personalized and nutritionally balanced weekly meal plans aligned with established dietary guidelines and food-group recommendations derived from Sustainable Healthy Diet (SHD) principles. The proposed approach is built upon the PLAN'EAT Expert-Curated Meal Database, a nutritionist-designed repository introduced in this work, comprising 401 expert-curated meals spanning Irish, Spanish, and Hungarian cuisines. Meal-plan generation follows a four-stage pipeline: (1) meal filtering based on country-specific cuisine, seasonality, dietary preferences, and allergies, (2) daily meal plan generation through large-scale sampling and scoring against expert-defined nutritional targets, (3) weekly meal plan assembly and optimization under nutritional and food-group constraints, and (4) diversity optimization to promote dietary variety while preserving nutritional validity. The proposed approach was validated through a large-scale in-silico validation involving 1,000 synthetic user profiles and the generation of 16,000 weekly meal plans corresponding to 112,000 daily meal plans. Adherence to daily and weekly nutritional targets, food-group constraints, and overall meal-plan validity at scale were assessed. In addition, we performed a preliminary descriptive evaluation against state-of-the-art Large Language Model (LLM)-based approaches under two complementary settings: Retrieval-Augmented Generation (RAG) and Supervised Fine-Tuning (SFT). Experimental results demonstrate that the proposed system can efficiently generate personalized, nutritionally compliant, and diverse weekly meal plans while maintaining transparent expert-rule-driven optimization. Furthermore, the descriptive evaluation against LLM approaches suggests that the proposed system achieves more consistent energy and macronutrient adherence under the evaluated conditions.
This research proposes an AI-driven personalized diet recommendation system that generates customized diet plans based on user health information and demonstrates the ability to generate safe and personalized diet recommendations.
Usha Kamale, M. Pujashree, T.Siri Chandana et al.· International Journal of Com...· 0 citations
The findings support the technical feasibility of the MAI-DIET framework and its preliminary acceptability among healthcare professionals, and further evaluation is required before conclusions can be drawn regarding its practical effectiveness.
Konstantinos Divanis, Alexandra Foscolou, Georgios Prosalentis et al.· Nutrients· 0 citations
A sustainable diet represents a multi-dimensional synergy among four essential pillars: nutrition adequacy, economic affordability, cultural acceptability, and environmental respect. Despite the prevalence of population-level sustainability modeling, practical implementation relies on effective individual-level adoption. This transition is often hindered by inter-individual heterogeneity, posing a formidable challenge in aligning sustainable diet requirements with individual preferences. To address this issue, we propose a personalized sustainable diet recommendation model based on a constraint-aware decision-making mechanism, where sustainability is incorporated through learnable constraints rather than modeled as user preferences. To systematically evaluate the proposed approach, we construct a sustainable diet dataset named SusDiet with about 150k recipes, characterized by broad coverage of sustainability indicators. Experimental results on this dataset show that our method promotes more sustainable choices without compromising individual preference. This work establishes a framework for aligning individual dietary choices with planetary health, offering quantitative evidence to guide future sustainable diet interventions and policy-making for sustainable development.
Ying Jin, Weiqing Min, Mingyu Huang et al.· 0 citations
This paper presents the development of a diet plan generator system that uses user-specific parameters such as age, weight, gender, activity level, dietary goals, and food preferences to recommend a structured meal plan.
Artificial Intelligence (AI) is revolutionizing health and nutrition, significantly advancing personalized dietary management and health optimization. Technologies like machine learning algorithms, data mining, and predictive models enable the creation of individualized diet plans based on genetic data, lifestyle habits, and biometric information, offering more effective and precise nutrition recommendations. AI-enabled systems provide personalized nutritional strategies for these diseases, offering real-time dietary suggestions tailored to individual needs. Additionally, AI optimizes nutritionists' and healthcare providers' recommendations by analyzing the effects of nutrition programs on health outcomes. In the food industry, AI promises advancements in food safety, quality control, product innovation, and sustainable food production. However, the widespread adoption of AI in nutrition faces challenges, including ethical concerns, data privacy issues, and algorithmic biases. Protecting personal health data and ensuring AI systems are fed with accurate, unbiased data are crucial in addressing these challenges. This review highlights AI's potential in nutrition science and health management, identifying opportunities for future research and innovation.
Hatice Karakoç, Eda Çaycı, Seda Çiftçi· Journal of NutriLife· 0 citations
Personalized nutrition recommendations encounter difficulties in aligning users′ dietary preferences with appropriate foods in extensive nutritional databases comprising thousands of products. This research introduces a multinutrient clustering framework that examines 8790 foods from the USDA National Nutrient Database for Standard Reference, Release 28, utilizing 23 nutritional attributes, including macronutrients, vitamins, and minerals. We thoroughly analyze K‐means and agglomerative clustering algorithms across various configurations (k = 2 − 8), finding that K‐means with eight clusters yields optimal performance, achieving a silhouette score of 0.273 and semantically interpretable dietary categories. The suggested technique shows a 46.4% improvement over suggestions based on popularity and a 273.0% improvement over recommendations based on a single nutrient. This was shown by a thorough evaluation utilizing Precision@K, NDCG, and fivefold cross‐validation (mean silhouette 0.265 ± 0.006). Users can set their own dietary preferences using nutrient sliders, six preset configurations (high protein, low carb, high fiber, low sodium, high calcium, and low calorie), and three configurable weighting strategies (equal, prioritized 3×, and focus‐only). The system then gives them real‐time recommendations (in less than 2 s) with clear similarity scores. A systematic examination with 50 automated test questions shows that the system works well in a wide range of dietary situations. The huge effect sizes (46.4% and 273.0% improvements) suggest that the results are statistically significant. This research connects computational nutrition studies with real‐world dietary advice. It offers an open‐source, understandable system for evidence‐based meal planning that fills important holes in current prediction and classification methods by allowing personalized, multinutrient food suggestions with clear reasoning behind the choices.
Rajkumar Sarker, Kazi Farhan Hasan Tanjim· International journal of foo...· 0 citations
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