ArticlePLOS digital health2025
AI-driven personalized nutrition: RAG-based digital health solution for obesity and type 2 diabetes.
Article in PLOS digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
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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
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Generative AI in Precision Nutrition: A Review of Current Developments and Future Directions.Nutrients · 2026Pooled it
- Comparative predictive modeling of pediatric spirometry reference equations in Jordanian children: Complex versus simple models.PLOS digital health · 2026Article
- Tailoring in eHealth lifestyle interventions targeting people with cardiometabolic conditions and lower socioeconomic position: A scoping review.PLOS digital health · 2026Article
- Applying Artificial Intelligence and machine learning in precision nutrition.Nature communications · 2026Review
- Navigating the AI era in dietetics: a qualitative analysis of professional identity, ethical concerns, and future projections in Türkiye.BMC health services research · 2026Article
- Improving self-care behaviors in patients with chronic obstructive pulmonary disease using a knowledge-based intelligent self-care system: protocol for a randomized controlled trial.Journal of thoracic disease · 2026Article
- Personalised health plan development using agentic AI in Singapore's national preventive care programme: a pilot study.NPJ digital medicine · 2026Article
- Toward an integrated animal-human nutrition intelligence via agentic retrieval-augmented language models.Frontiers in nutrition · 2026Article
- An AI-driven multivariate approach for personalized healthy eating recommendations aligned with sustainable healthy diet food-group guidelines.Frontiers in nutrition · 2026Article
- Review
- Is AI overhyped?Patterns (New York, N.Y.) · 2025Article
- Obesity as a Multifactorial Chronic Disease: Molecular Mechanisms, Systemic Impact, and Emerging Digital Interventions.Current issues in molecular biology · 2025Review
- Integrating Precision Medicine and Digital Health in Personalized Weight Management: The Central Role of Nutrition.Nutrients · 2025Review
- Article
- Article
- Research on artificial intelligence in stress and obesity: A bibliometric analysis using scopus, web of science, PubMed.Digital healthReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Effective management of obesity and type 2 diabetes is a major global public health challenge that requires evidence-based, scalable personalized nutrition solutions. Here, we present an artificial intelligence (AI) driven dietary recommendation system that generates personalized smoothie recipes while prioritizing health outcomes and environmental sustainability. A key feature of the system is the "virtual nutritionist", an iterative validation framework that dynamically refines recipes to meet predefined nutritional and sustainability criteria. The system integrates dietary guidelines from the National Institute for Public Health and the Environment (RIVM), EUFIC, USDA FoodData Central, and the American Diabetes Association with retrieval-augmented generation (RAG) to deliver evidence-based recommendations. By aligning with the United Nations Sustainable Development Goals (SDGs), the system promotes plant-based, seasonal, and locally sourced ingredients to reduce environmental impact. We leverage explainable AI (XAI) to enhance user engagement through clear explanations of ingredient benefits and interactive features, improving comprehension across varying health literacy levels. Using zero-shot and few-shot learning techniques, the system adapts to user inputs while maintaining privacy through local deployment of the LLaMA3 model. In evaluating 1,000 recipes, the system achieved 80.1% adherence to health guidelines meeting targets for calories, fiber, and fats and 92% compliance with sustainability criteria, emphasizing seasonal and locally sourced ingredients. A prototype web application enables real-time, personalized recommendations, bridging the gap between AI-driven insights and clinical dietary management. This research underscores the potential of AI-driven precision nutrition to revolutionize chronic disease management by improving dietary adherence, enhancing health literacy, and offering a scalable, adaptable solution for clinical workflows, telehealth platforms, and public health initiatives, with the potential to significantly alleviate the global healthcare burden.
Identifiers
What OpenQuestion holds
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.