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Relevant bibliographies by topics / AI-based personalized nutrition

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Academic literature on the topic 'AI-based personalized nutrition'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "AI-based personalized nutrition"

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NARSIMHA REDDY |, MALLU. "“Spontaneity: Personalized Nutrition and Meal Planning”." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45147.

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Having a nutritious diet in the midst of busy lifestyles, diet restrictions, and limited cooking skills is a challenge for the masses. Spoontaneity caters to these challenges by providing AI- based customized nutrition plans and meal delivery solutions. By way of a simple online platform, customers can get personalized diet tips, customized meal plans, and fresh meal deliveries, improving their wellness experience. Spoontaneity utilizes bleeding-edge AI and machine learning (ML) technologies to study consumers' dietary aspirations, health targets, and life restrictions, offering accurate meal

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Ter, Zheng Bin, Naveen Palanichamy, and Jayapradha J. "Generative AI-based Meal Recommender System." Journal of Informatics and Web Engineering 4, no. 2 (2025): 315–38. https://doi.org/10.33093/jiwe.2025.4.2.20.

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Maintaining a balanced diet is essential for overall well-being, yet many individuals face challenges in meal planning due to time constraints, limited nutritional knowledge, and difficulty aligning meals with personal dietary needs. Traditional meal recommender systems often rely on predefined plans or collaborative filtering techniques, limiting their adaptability and personalization. This study presents a generative AI-based Meal Recommender System utilizing Variational Autoencoders (VAEs) to generate personalized and nutritionally balanced meal plans. The system processes user inputs, such

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Kumar, Manish, Kumari Sonam,, and Kundan Kumar Kar. "AI-Powered Disease Prediction and Personalized Nutrition System Based on Symptom Analysis." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44068.

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In the digital era, disease diagnosis and patient management is taking a decisive leap forward to dynamically link personalized medical decision making, disease recognition and management with the massive individuality and uniqueness of the human body. Personalized nutrition utilizes user-specific data, including genetic, metabolic and lifestyle factors to optimize dietary recommendations. Traditional dietary guidelines are often generic and fail to consider individual differences, leading to suboptimal health outcomes. This study examines the role of artificial intelligence (AI), machine lear

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Bhatt, Aastha. "Dietify - AI Based Diet & Nutrition Consultation Application." International Journal for Research in Applied Science and Engineering Technology 13, no. 6 (2025): 2408–15. https://doi.org/10.22214/ijraset.2025.72653.

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This paper provides a detailed review of Dietify, an AI-based application designed to offer personalized diet and nutrition consultations. Utilizing machine learning and predictive analytics, Dietify provides real-time dietary recommendations, continuously adapting meal plans based on user feedback and health objectives. This review examines Dietify’s functional architecture, methodology, advantages, and limitations. Furthermore, it situates Dietify within the broader field of AI-driven diet management applications and discusses future opportunities, including expanded integration with wearabl

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Sumathi, Dr P. "Track Nutrition Using Gen AI." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45398.

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Abstract - In this paper, we present a novel system that enables users to track their daily nutrition using Generative AI (Gen AI). Traditional methods of food tracking involve manual logging, which is time-consuming and prone to human error. Our solution leverages generative models to simplify this process by allowing users to input meals using natural language or images. The system intelligently processes these inputs using large language models (LLMs) and computer vision (CV) to extract nutritional details. Personalized recommendations are provided based on the user's health goals and dieta

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Amiri, Maryam, Juan Li, and Wordh Hasan. "Personalized Flexible Meal Planning for Individuals With Diet-Related Health Concerns: System Design and Feasibility Validation Study." JMIR Formative Research 7 (August 3, 2023): e46434. http://dx.doi.org/10.2196/46434.

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Background Chronic diseases such as heart disease, stroke, diabetes, and hypertension are major global health challenges. Healthy eating can help people with chronic diseases manage their condition and prevent complications. However, making healthy meal plans is not easy, as it requires the consideration of various factors such as health concerns, nutritional requirements, tastes, economic status, and time limits. Therefore, there is a need for effective, affordable, and personalized meal planning that can assist people in choosing food that suits their individual needs and preferences. Object

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Vegesna, Dr Vinod. "AI-Driven Personalized Nutrition: A system for Tailored Dietary Recommendations." International Research Journal of Computer Science 11, no. 07 (2024): 545–50. http://dx.doi.org/10.26562/irjcs.2024.v1107.02.

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Employing deep neural networks (DNNs) and machine learning (ML) to provide individualised nutrition programs based on user demands, AI-driven personalised nutrition is a revolutionary approach to nutritional recommendations. Nutritional intake, metabolic reactions, and dietary patterns have all been studied in the past using classical machine learning (ML) techniques including Random Forests (RF), Support Vector Machines (SVM), and k-Nearest Neighbours (k-NN). Though useful in identifying some dietary patterns, these models frequently suffer from the complexity and high dimensionality of nutri

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Sonia, S. V. Evangelin, Jebakumari Sutha.A, V. Nisha, et al. "AI-Driven Personalized Health & Nutrition Assistant Using DeepSeek and LLaVA." Journal of Neonatal Surgery 14, no. 9S (2025): 668–74. https://doi.org/10.52783/jns.v14.2734.

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Traditional medical care failed to provide the personalized individualized analysis which makes it hard to get individualised wellness advice. Artificial intelligence writes down data analysis to personalize health advice from intelligent systems that solves the problem of simply giving general health care advice. According to this project, DeepSeek is coming together with LLaVA in order to develop better AI-based nutrition and wellness guidance. Users can access structured profiles containing BMI measurements, diet patterns, physical exercise, and medical health conditions (such as pressure)

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Saxena, Ritcha, Vikas Sharma, Ananya Raj Saxena, and Aakash Patel. "Harnessing AI and Gut Microbiome Research for Precision Health." Journal of Artificial Intelligence General science (JAIGS) ISSN:3006-4023 3, no. 1 (2024): 74–88. http://dx.doi.org/10.60087/jaigs.v3i1.68.

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The gut microbiome's impact on physiological processes, influenced by diet and lifestyle, is profound. Dysbiosis, an imbalance in microbiota composition, is associated with diseases like obesity. This review explores the gut microbiome's role in metabolism and calorie intake, alongside recent AI advancements impacting personalized nutrition. AI has revolutionized microbiome research, especially in multi-omics data analysis. AI-driven approaches enable the integration and interpretation of diverse omics datasets, including genomics, metabolomics, and proteomics, providing comprehensive insights

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Rouskas, Konstantinos, Mary Guela, Marianna Pantoura, et al. "The Influence of an AI-Driven Personalized Nutrition Program on the Human Gut Microbiome and Its Health Implications." Nutrients 17, no. 7 (2025): 1260. https://doi.org/10.3390/nu17071260.

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Background/Objectives: Personalized nutrition programs enhanced with artificial intelligence (AI)-based tools hold promising potential for the development of healthy and sustainable diets and for disease prevention. This study aimed to explore the impact of an AI-based personalized nutrition program on the gut microbiome of healthy individuals. Methods: An intervention using an AI-based mobile application for personalized nutrition was applied for six weeks. Fecal and blood samples from 29 healthy participants (females 52%, mean age 35 years) were collected at baseline and at six weeks. Gut mi

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Book chapters on the topic "AI-based personalized nutrition"

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Eswaran, Ushaa, Vivek Eswaran, Keerthna Murali, and Vishal Eswaran. "AI-Powered Personalized Nutrition: How AI Can Customize Nutrition Plans Based on Individual PCOS Profiles." In AI-Based Nutritional Intervention in Polycystic Ovary Syndrome (PCOS). Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2120-0_4.

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Singh, Bhupinder, and Anand Nayyar. "Mobile Applications and Wearable in PCOS Nutrition: mHealth Solutions and Personalized Health Monitoring." In AI-Based Nutritional Intervention in Polycystic Ovary Syndrome (PCOS). Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2120-0_12.

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Gupta, Anshika, and Kalpana Katiyar. "Supplements and Herbal Remedies: An Exploration of Nutritional Supplements and Herbal Remedies for PCOS and AI’s Role in Personalized Recommendations." In AI-Based Nutritional Intervention in Polycystic Ovary Syndrome (PCOS). Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2120-0_10.

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S., Karkuzhali, and Senthilkumar S. "AI-Driven Personalized Nutritional and Scams Planning." In Neuroscientific Insights and Therapeutic Approaches to Eating Disorders. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-3230-6.ch002.

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As advancements in artificial intelligence (AI) continue to revolutionize various industries, personalized nutritional planning emerges as a promising application in the realm of healthcare and wellness. This chapter delves into the intersection of AI-driven personalized nutritional planning and the regulatory landscape governing food regulations, laws, and potential scams. By leveraging AI algorithms, individuals can receive tailored dietary recommendations based on their unique health profiles, genetic makeup, and lifestyle factors. However, amidst this innovation, ensuring compliance with f

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Priyadharshini, K., K. Dhivya, M. S. Kamalesh, S. J. Suji Prasad, Deeban Chakravarthy, and M. Sudhakar. "Personalized Nutrition in Healthcare Using IoT for Tailored Dietary Solutions." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-7112-1.ch019.

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Personalized nutrition is precision health that forms personalized diets based on the genetic, environmental, and lifestyle characteristics of an individual. It further improves with the integration of Internet of Things in collecting, analyzing, and feedback mechanisms in real time, enhancing the precision and adaptation of nutritional interventions: glucose levels, body composition, and diet are monitored with wearables, smart appliances, and connected health systems. The data, thus processed, is then channeled through AI algorithms to derive personal recommendations that are tailored to the

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Banka, Raksha, Poulomi Das, Suparna Das, and Joyeta Ghosh. "Tailored Nutrition and Diet Plan Using AI and Machine Learning for Precision Wellness." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-9521-9.ch007.

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Tailored nutrition leverages Artificial Intelligence (AI) and Machine Learning (ML) to deliver personalized dietary recommendations based on individual factors like genetics, lifestyle, and metabolic responses. These technologies integrate diverse datasets, such as genomics and microbiome profiles, enabling dynamic and precise diet planning. Key advancements include deep learning models for metabolic prediction and wearable devices for continuous monitoring. Despite challenges like data privacy and accessibility, AI-driven solutions offer transformative potential in preventing chronic diseases

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Lee, Hsiu-An, Chen-Yi Liu, and Chien-Yeh Hsu. "Precision Nutrition Management in Continuous Care: Leveraging AI for User-Reported Dietary Data Analysis." In Studies in Health Technology and Informatics. IOS Press, 2024. http://dx.doi.org/10.3233/shti240148.

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A As health technology advances, this study aims to develop an innovative nutritional intake management system that integrates artificial intelligence technology and social media software to achieve precise analysis of patient-generated data and comprehensive management in continuous care. Our system is built on the Line Bot platform, allowing users to easily and intuitively obtain detailed analyses of their individual nutritional intake by reporting dietary information. While users report their dietary habits through the Line Bot, our AI model conducts real-time analysis of nutrient intake, p

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Theodorakis, Nikolaos, Magdalini Kreouzi, Andreas Pappas, et al. "The Complexities of Metabolic Flexibility and Precision Approaches to Sustainable Weight Management." In Innovations in Precision Medicine and Genomics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-5787-3.ch005.

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The global rise in obesity highlights the need for personalized weight management strategies that account for individual metabolic and hormonal differences, moving beyond the simplistic “calories in, calories out” approach. Body types—ectomorph, mesomorph, and endomorph—serve as a framework for understanding variations in fat storage, muscle development, and energy expenditure. These differences are influenced by genetic, epigenetic, and lifestyle factors, including nutrition, exercise, sleep, and stress. Such factors affect processes like lipogenesis, myofibrillar protein synthesis during ove

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Cenikj, Gjorgjina, Mauro Dragoni, Tome Eftimov, et al. "Neurosymbolic Methods for Food Computing." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2025. https://doi.org/10.3233/faia250242.

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In this chapter, we explore the integration of symbolic reasoning and neural network-based methods within the domain of food science. We provide a comprehensive overview of symbolic methods such as food ontologies, knowledge graphs, and their construction, emphasizing their role in enhancing data interoperability and supporting complex food computing tasks. We then discuss neural network-based techniques for extracting food information from textual and image data, food representation learning through embeddings, and present various methodologies for food category classification, nutrition esti

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Tsatsou, Dorothea, Elena Lalama, Saskia L. Wilson-Barnes, et al. "NAct: The Nutrition & Activity Ontology for Healthy Living." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210377.

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This paper presents the NAct (Nutrition & Activity) Ontology, designed to drive personalised nutritional and physical activity recommendations and effectively support healthy living, through a reasoning-based AI decision support system. NAct coalesces nutritional, medical, behavioural and lifestyle indicators with potential dietary and physical activity directives. The paper presents the first version of the ontology, including its co-design and engineering methodology, along with usage examples in supporting healthy nutritional and physical activity choices. Lastly, the plan for future im

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Conference papers on the topic "AI-based personalized nutrition"

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Ilukpitiya, I. M. D. J. R. B., H. M. R. B. Herath, R. H. M. S. A. Rajakaruna, M. H. S. M. Herath, Koliya Pulasinghe, and Jenny Krishara. "AI-Driven Personalized Fitness Coaching with Body Type-Based Workout and Nutrition Plans and Real-Time Exercise Feedback." In 2024 International Conference on Computer and Applications (ICCA). IEEE, 2024. https://doi.org/10.1109/icca62237.2024.10928121.

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Azzimani, Khalid, Hayat Bihri, Asma Dahmi, Salma Azzouzi, and My El Hassan Charaf. "An AI Based Approach for Personalized Nutrition and Food Menu Planning." In 2022 IEEE 3rd International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS). IEEE, 2022. http://dx.doi.org/10.1109/icecocs55148.2022.9983099.

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Kinsey, Sarah, Jack Wolf, Nalini Saligram, et al. "Building a Personalized Messaging System for Health Intervention in Underprivileged Regions Using Reinforcement Learning." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/668.

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This work builds an effective AI-based message generation system for diabetes prevention in rural areas, where the diabetes rate has been increasing at an alarming rate. The messages contain information about diabetes causes and complications and the impact of nutrition and fitness on preventing diabetes. We propose to apply reinforcement learning (RL) to optimize our message selection policy over time, tailoring our messages to align with each individual participant's needs and preferences. We conduct an extensive field study in a large country in Asia which involves more than 1000 participan

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Gavai, Anand K. "Personalized and Sustainable Smoothie Generation for Metabolic Disease Management." In The Second International Conference of AI new Technology and open Discussion. Algorithm Lab, 2025. https://doi.org/10.63211/j.p.25.645341.

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While AI has shown promise in dietary recommendations, existing systems often lack personalization and evidence-based validation for chronic disease management. This study presents an innovative AI-driven dietary recommendation system that leverages Retrieval Augmented Generation (RAG) to create customized smoothie recipes for people with obesity and type 2 diabetes. The system uniquely combines the generative capabilities of large language models (LLaMA3) with real-time data integration through the USDA's FoodData Central API, while incorporating the Dutch dietary guidelines (RIVM) and the Eu

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Stahl, Christoph, Krizia Ferrini, and Torsten Bohn. "LIFANA – User-centered design of a personalized meal recommender app for the elderly." In Human Interaction and Emerging Technologies (IHIET-AI 2024). AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1004551.

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As the global population continues to age, the healthcare and technology sectors have witnessed an increased interest in understanding and addressing the unique needs of the elderly population. The health challenges faced by the elderly are multifaceted and often interconnected. Age-related cognitive decline can impair memory and decision-making abilities, while physical frailty, with reduced muscle strength and bone density, raises the risk of falls and fractures, affecting their quality of life. Comprehensive healthcare strategies that focus on prevention, early intervention are crucial to a

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