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Article . 2025 . Peer-reviewed
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AI-driven personalized nutrition: RAG-based digital health solution for obesity and type 2 diabetes

RAG-based digital health solution for obesity and type 2 diabetes
Authors: Anand K. Gavai; Jos van Hillegersberg;

AI-driven personalized nutrition: RAG-based digital health solution for obesity and type 2 diabetes

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.

Country
Netherlands
Keywords

UT-Gold-D, SDG 17 - Partnerships for the Goals, SDG 3 - Good Health and Well-being, SDG 8 - Decent Work and Economic Growth, SDG 3 – Goede gezondheid en welzijn, Research Article

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    influence
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
14
Top 10%
Top 10%
Top 10%
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gold
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