ArticleNutrients2022
PROTEIN AI Advisor: A Knowledge-Based Recommendation Framework Using Expert-Validated Meals for Healthy Diets.
Article in Nutrients, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it, 49 citations in OpenAlex.
- Pooled it
- A Systematic Review on Food Recommender Systems for Diabetic Patients.International journal of environmental research and public health · 2023Pooled it
- Effect of a Personalized Mobile App on Glucose Control in Adults With Prediabetes and Type 2 Diabetes: Exploratory Pilot Randomized Controlled Trial.JMIR human factors · 2026Trial
- D-pFoodReQ: Answer-Frequency-Aware Debiasing for Constrained Knowledge-Base Question Answering in Food Recommendation.Foods (Basel, Switzerland) · 2026Article
- Article
- Applying Artificial Intelligence and machine learning in precision nutrition.Nature communications · 2026Review
- Artificial Intelligence in the Nutritional Management of Inflammatory Bowel Disease: A Scoping Review.Journal of multidisciplinary healthcare · 2026Review
- An AI-driven multivariate approach for personalized healthy eating recommendations aligned with sustainable healthy diet food-group guidelines.Frontiers in nutrition · 2026Article
- Ontology-based dietary recommendation system for Chinese children and adolescents: development and a pilot validation study.Frontiers in public health · 2026Article
- SWITCHtoHEALTHY AI-Based Family Nutrition Recommendation System: Promoting the Mediterranean Diet.Nutrients · 2025Article
- The Influence of an AI-Driven Personalized Nutrition Program on the Human Gut Microbiome and Its Health Implications.Nutrients · 2025Article
- Diet Quality and Caloric Accuracy in AI-Generated Diet Plans: A Comparative Study Across Chatbots.Nutrients · 2025Article
- Investigation and Assessment of AI's Role in Nutrition-An Updated Narrative Review of the Evidence.Nutrients · 2025Review
- An AI-based nutrition recommendation system: technical validation with insights from Mediterranean cuisine.Frontiers in nutrition · 2025Article
- Editorial: Smart devices for personalized nutrition and healthier lifestyle behavior change.Frontiers in nutrition · 2025Article
- Perspective on the ethics of AI at the intersection of nutrition and behaviour change.Frontiers in aging · 2025Article
- Personal Goals, User Engagement, and Meal Adherence within a Personalised AI-Based Mobile Application for Nutrition and Physical Activity.Life (Basel, Switzerland) · 2024Article
- Article
- A practical guide to the implementation of AI in orthopaedic research, Part 6: How to evaluate the performance of AI research?Journal of experimental orthopaedics · 2024Review
- AI nutrition recommendation using a deep generative model and ChatGPT.Scientific reports · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
23 authors at 10 institutions in 6 countries.
Funding
Abstract
AI-based software applications for personalized nutrition have recently gained increasing attention to help users follow a healthy lifestyle. In this paper, we present a knowledge-based recommendation framework that exploits an explicit dataset of expert-validated meals to offer highly accurate diet plans spanning across ten user groups of both healthy subjects and participants with health conditions. The proposed advisor is built on a novel architecture that includes (a) a qualitative layer for verifying ingredient appropriateness, and (b) a quantitative layer for synthesizing meal plans. The first layer is implemented as an expert system for fuzzy inference relying on an ontology of rules acquired by experts in Nutrition, while the second layer as an optimization method for generating daily meal plans based on target nutrient values and ranges. The system's effectiveness is evaluated through extensive experiments for establishing meal and meal plan appropriateness, meal variety, as well as system capacity for recommending meal plans. Evaluations involved synthetic data, including the generation of 3000 virtual user profiles and their weekly meal plans. Results reveal a high precision and recall for recommending appropriate ingredients in most user categories, while the meal plan generator achieved a total recommendation accuracy of 92% for all nutrient recommendations.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.