{"title":"Intelligent Diet Recommendation System Powered by Artificial Intelligence for Personalized Nutritional Solutions.","authors":"Tohid Amadeh, Matin Rafie, Shadmehr Radmanesh, Alireza Azizi, Ahmadreza Ahangarian, Pourya Fathollahi, Hadise Ahmadloo","doi":"10.1016/j.clnesp.2025.09.002","DOIUrl":null,"url":null,"abstract":"<p><strong>Background and aims: </strong>The increasing number of non-communicable diseases, such diabetes and obesity, makes it even more important to have accurate and personalized dietary solutions. Based on a lot of research, standard diet advice may not be accurate enough to meet individual health demands. The Intelligent Diet Recommendation System is an artificial intelligence-powered platform that gives personalized dietary recommendations based on extensive body composition data and cultural eating habits.</p><p><strong>Methods: </strong>The Intelligent Diet Recommendation System gathers key measurements, including body mass index and body fat percentage, using cutting-edge body analysis tools. Customized diets were created using 3D body modeling technologies and machine learning algorithms. The system's performance was evaluated by assessing the inaccuracy rate of its dietary recommendations.</p><p><strong>Results: </strong>The Intelligent Diet Recommendation System made personalized diet plans based on physiological and cultural factors with an error rate of less than 3%.</p><p><strong>Conclusions: </strong>The results show that the Intelligent Diet Recommendation System is a scalable, artificial intelligence-based way to solve global health problems that makes dietary advice much more accurate and easy to find. This system offers a new way of doing nutritional therapy that could improve health outcomes around the world.</p>","PeriodicalId":10352,"journal":{"name":"Clinical nutrition ESPEN","volume":" ","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2025-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical nutrition ESPEN","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1016/j.clnesp.2025.09.002","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"NUTRITION & DIETETICS","Score":null,"Total":0}
引用次数: 0
Abstract
Background and aims: The increasing number of non-communicable diseases, such diabetes and obesity, makes it even more important to have accurate and personalized dietary solutions. Based on a lot of research, standard diet advice may not be accurate enough to meet individual health demands. The Intelligent Diet Recommendation System is an artificial intelligence-powered platform that gives personalized dietary recommendations based on extensive body composition data and cultural eating habits.
Methods: The Intelligent Diet Recommendation System gathers key measurements, including body mass index and body fat percentage, using cutting-edge body analysis tools. Customized diets were created using 3D body modeling technologies and machine learning algorithms. The system's performance was evaluated by assessing the inaccuracy rate of its dietary recommendations.
Results: The Intelligent Diet Recommendation System made personalized diet plans based on physiological and cultural factors with an error rate of less than 3%.
Conclusions: The results show that the Intelligent Diet Recommendation System is a scalable, artificial intelligence-based way to solve global health problems that makes dietary advice much more accurate and easy to find. This system offers a new way of doing nutritional therapy that could improve health outcomes around the world.
期刊介绍:
Clinical Nutrition ESPEN is an electronic-only journal and is an official publication of the European Society for Clinical Nutrition and Metabolism (ESPEN). Nutrition and nutritional care have gained wide clinical and scientific interest during the past decades. The increasing knowledge of metabolic disturbances and nutritional assessment in chronic and acute diseases has stimulated rapid advances in design, development and clinical application of nutritional support. The aims of ESPEN are to encourage the rapid diffusion of knowledge and its application in the field of clinical nutrition and metabolism. Published bimonthly, Clinical Nutrition ESPEN focuses on publishing articles on the relationship between nutrition and disease in the setting of basic science and clinical practice. Clinical Nutrition ESPEN is available to all members of ESPEN and to all subscribers of Clinical Nutrition.