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人工智能驱动的个性化营养:基于检索增强生成的肥胖和2型糖尿病数字健康解决方案。

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

作者信息

Gavai Anand K, van Hillegersberg Jos

机构信息

Industrial Engineering & Business Information Systems, University of Twente, Enschede, The Netherlands.

Jheronimus Academy of Data Science, 's-Hertogenbosch, The Netherlands.

出版信息

PLOS Digit Health. 2025 May 6;4(5):e0000758. doi: 10.1371/journal.pdig.0000758. eCollection 2025 May.

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.

摘要

有效管理肥胖症和2型糖尿病是一项重大的全球公共卫生挑战,需要基于证据的、可扩展的个性化营养解决方案。在此,我们展示了一种由人工智能(AI)驱动的饮食推荐系统,该系统在优先考虑健康结果和环境可持续性的同时生成个性化的奶昔食谱。该系统的一个关键特性是“虚拟营养师”,这是一个迭代验证框架,可动态优化食谱以满足预定义的营养和可持续性标准。该系统将荷兰国家公共卫生与环境研究所(RIVM)、欧洲食品信息理事会(EUFIC)、美国农业部食品数据中心(USDA FoodData Central)以及美国糖尿病协会的饮食指南与检索增强生成(RAG)相结合,以提供基于证据的建议。通过与联合国可持续发展目标(SDGs)保持一致,该系统推广以植物为基础、季节性和本地采购的食材,以减少对环境的影响。我们利用可解释人工智能(XAI),通过清晰解释食材益处和交互式功能来增强用户参与度,提高不同健康素养水平用户的理解能力。使用零样本和少样本学习技术,该系统适应用户输入,同时通过本地部署LLaMA3模型来维护隐私。在评估1000份食谱时,该系统实现了80.1%符合健康指南,满足卡路里、纤维和脂肪目标,以及92%符合可持续性标准,强调季节性和本地采购的食材。一个原型网络应用程序能够提供实时、个性化的建议,弥合了人工智能驱动的见解与临床饮食管理之间的差距。这项研究强调了人工智能驱动的精准营养在改善饮食依从性、提高健康素养以及为临床工作流程、远程医疗平台和公共卫生倡议提供可扩展、适应性强的解决方案方面,有可能彻底改变慢性病管理,从而显著减轻全球医疗负担。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0484/12054865/10f3151f91a7/pdig.0000758.g001.jpg

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