TL;DR
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.
Maintaining a balanced and nutritious diet is essential for preventing lifestyle-related diseases and enhancing overall well-being. However, conventions diet planning approaches often provide generalized guidelines that do not account for individual health conditions, allergies, lifestyle habits, or personal food preferences. Because of this, many individuals follow diet plans that may not be suitable for their specific nutritional needs. Recent advancements in artificial intelligence and machine learning have enabled systems to generate personalized recommendations. This research proposes an AI-driven personalized diet recommendation system that generates customized diet plans based on user health information. The system collects parameters such as age, gender, height, weight, dietary preferences, allergies, and medical conditions. A Random Forest algorithm is employed to determine the most appropriate diet category. A rule-based filtering mechanism is applied to ensure that the recommended diet follows medical guidelines and avoids foods that may be harmful. The system also calculates important health indicators such as Body Mass Index (BMI), Basal Metabolic Rate (BMR), and daily calorie requirements to support accurate nutritional analysis. In addition, an AI chat-bot is integrated to provide dietary guidance and answer user queries related to nutrition. The system is implemented as a web application using the Flask framework and demonstrates the ability to generate safe and personalized diet recommendations.
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.
The increased rate of nutrition and lifestyle-related diseases has caused an urgent need for intelligent and personalised nutrition management systems. The traditional approaches to dietary recommendations are primarily based on universal meal planning guidelines without addressing specific health needs, requirements, preferences, fitness objectives, or restrictions on access to foods. As a result, in this paper, a web-based personalized nutrition recommendation framework based on Artificial Intelligence (AI) is proposed in order to overcome these drawbacks. The user health and nutrition datasets are first preprocessed by Robust Scaler (RS) to reduce the effect of outliers and to normalize the features. Then, the Autoencoder learns deep dietary features and dietary patterns. Moreover, Select from Model (SFM) selects the most important nutritional features to be optimized so as to achieve a higher recommendation efficiency and a higher accuracy in the prediction. Finally, the XGBoost model is deployed in the AI recommendation engine where it classifies dietary goals and provides personalized meal recommendations, sending them to the web dashboard, chatbot, and ordering module. The experimental results demonstrate the superiority of accuracy, precision, recall, F1-score, recommendation reliability and long-term dietary adherence when compared to a traditional approach.
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.
BACKGROUND Personalized meal planning by registered dietitian nutritionists (RDNs) is time-intensive. Large language models (LLMs) may automate drafting meal plans, but their nutritional accuracy in clinical practice is uncertain. METHODS In this proof-of-concept study, five outpatient RDNs and four LLMs (Gemini, CoPilot, ChatGPT 4.0, and customized ChatGPT 4.0) each generated 3-day meal plans for five validated clinical scenarios. Effectiveness was defined as accuracy in meeting pre-specified energy, protein, carbohydrate, fat, and sodium targets. Time to create plans and RDN comfort (self-rated confidence in nutritional accuracy and clinical appropriateness on 1-5 Likert scale) were recorded. Three independent RDNs, blinded to source, analyzed nutrient content using Nutritionist Pro. Group differences were assessed with t-test and ANOVA. RESULTS All LLMs and RDNs produced feasible meal plans. LLMs generated meal plans in under 1 min, whereas RDNs required a mean of 44 min per scenario. RDNs reported comfort levels ranging from 3.8 to 4.8. Across most scenarios, LLM plans delivered a smaller proportion of requested energy than RDN plans, which more consistently approached energy targets. Both groups performed similarly for the Mediterranean diet scenario. Overall, protein accuracy did not differ. However, in chronic kidney disease, LLMs undershot the guideline-based protein target, while RDNs tended to modestly exceed it. Accuracy for low-carbohydrate, fat, and sodium diets was comparable. CONCLUSION LLMs can rapidly generate clinically plausible meal plans but are less reliable than RDNs in achieving prescribed energy and selected macronutrient goals. Prompt precision is essential for nutrient-specific targets. A hybrid model in which RDNs refine LLM-generated drafts may leverage efficiency without sacrificing clinical accuracy.