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

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

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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Gavai, Anand K., and Jos van Hillegersberg. "AI-driven personalized nutrition: RAG-based digital health solution for obesity and type 2 diabetes." PLOS Digital Health 4, no. 5 (2025): e0000758. https://doi.org/10.1371/journal.pdig.0000758.

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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 t

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Theodore Armand, Tagne Poupi, Kintoh Allen Nfor, Jung-In Kim, and Hee-Cheol Kim. "Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review." Nutrients 16, no. 7 (2024): 1073. http://dx.doi.org/10.3390/nu16071073.

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In industry 4.0, where the automation and digitalization of entities and processes are fundamental, artificial intelligence (AI) is increasingly becoming a pivotal tool offering innovative solutions in various domains. In this context, nutrition, a critical aspect of public health, is no exception to the fields influenced by the integration of AI technology. This study aims to comprehensively investigate the current landscape of AI in nutrition, providing a deep understanding of the potential of AI, machine learning (ML), and deep learning (DL) in nutrition sciences and highlighting eventual c

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Jiménez-González, Carolina, Marta Alonso-Peña, Paula Argos Vélez, Javier Crespo, and Paula Iruzubieta. "Unraveling MASLD: The Role of Gut Microbiota, Dietary Modulation, and AI-Driven Lifestyle Interventions." Nutrients 17, no. 9 (2025): 1580. https://doi.org/10.3390/nu17091580.

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Gut microbiota has a crucial role in the pathophysiology of metabolic-associated steatotic liver disease (MASLD), influencing various metabolic mechanisms and contributing to the development of the disease. Dietary interventions targeting gut microbiota have shown potential in modulating microbial composition and mitigating MASLD progression. In this context, the integration of multi-omics analysis and artificial intelligence (AI) in personalized nutrition offers new opportunities for tailoring dietary strategies based on individual microbiome profiles and metabolic responses. The use of chatb

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Manish Kumar Sharma, Anmol Gupta, and Yatharth Chauhan. "Harnessing Artificial Intelligence for Predictive Analytics in Culinary Trends, Personalized Nutrition, and Automated Cooking Systems: A Technological Shift in Future Food Innovation." International Journal for Multidimensional Research Perspectives 3, no. 6 (2025): 13–25. https://doi.org/10.61877/ijmrp.v3i6.286.

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This study explores the transformative impact of Artificial Intelligence (AI) on the food industry, particularly in predictive analytics for culinary trends, personalized nutrition, and automated cooking systems. AI's ability to process vast datasets and generate actionable insights is revolutionizing how food businesses predict and adapt to evolving consumer preferences and dietary trends. Predictive analytics, powered by machine learning algorithms, enables businesses to anticipate market shifts, ensuring timely product development and minimizing food waste. In personalized nutrition, AI is

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Gowda, Suhas K., Vinay Nagasara R, Srujan SS, and Vishawas UR. "A Comprehensive Review on AI-Based Food Image Analysis and Dietary Recommendation Systems." International Journal of All Research Education and Scientific Methods 13, no. 05 (2025): 1946–50. https://doi.org/10.56025/ijaresm.2025.1305251946.

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The global rise in nutrition-related health issues like obesity, diabetes, and cardiovascular diseases, along with increased awareness of healthy eating, has accelerated the development of AI-based systems for food monitoring and dietary guidance. This review presents recent progress in food image analysis and recommendation technologies since 2022, emphasizing AI methods for segmentation, classification, and personalized nutrition. Benchmark datasets such as Food101, UEC-Food256, and VIREO Food-172 are evaluated for their impact on model performance. Challenges related to annotation cost, mod

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Stoian, Mircea, Adina Andone, Sergiu Rareș Bândilă, et al. "Personalized Nutrition Strategies for Patients in the Intensive Care Unit: A Narrative Review on the Future of Critical Care Nutrition." Nutrients 17, no. 10 (2025): 1659. https://doi.org/10.3390/nu17101659.

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Introduction: Critically ill patients in intensive care units (ICUs) are at high risk of malnutrition, which can result in muscle atrophy, polyneuropathy, increased mortality, or prolonged hospitalizations with complications and higher costs during the recovery period. They often develop ICU-acquired weakness, exacerbated by sepsis, immobilization, and drug treatments, leading to rapid muscle mass loss and long-term complications. Studies indicate that adequate protein and calorie intake can decrease mortality and improve prognosis and recovery. However, optimal implementation remains a critic

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Mishra, Yashraj. "AI Powered Weight Based Meal Recommendation QnA Chatbot using Pre-Trained Language Model." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 1670–75. https://doi.org/10.22214/ijraset.2025.70549.

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In recent years, artificial intelligence (AI) has revolutionized personalized health and nutrition systems by integrating machine learning and natural language processing (NLP). This research introduces an AI-powered, weight-based meal recommendation and question-answering (QnA) chatbot system, developed using pre-trained transformer models (T5-base and T5-large). The system aims to assist users in receiving personalized dietary recommendations based on gender and BMI-based weight categories (Underweight, Normal weight, Overweight, Obesity) and offers interactive responses to common health-rel

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Bharadwaj, Abhishek. "Revolutionizing perioperative medicine: Technological advancements for enhanced recovery." Serbian Journal of Anesthesia and Intensive Therapy 47, no. 1-2 (2025): 5–16. https://doi.org/10.5937/sjait2502005b.

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Enhanced Recovery After Surgery (ERAS) is a multimodal, evidence-based approach designed to minimize surgical stress, accelerate recovery, and improve patient outcomes. Recent technological advancements have transformed ERAS protocols by integrating robotic-assisted surgery, artificial intelligence (AI)-driven monitoring, advanced thermoregulation, automated nutrition management, and real-time perioperative decision support. These innovations enhance surgical precision, metabolic stability, pain control, and personalized patient care, ultimately reducing complications and improving recovery ef

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Nikitjuk, Dmitriy B., Margarita M. Korosteleva, and Inna Yu Tarmaeva. "Sports nutrition as an example of effective implementation of innovative trends in nutrition: personalization and digitalization (literature review)." HEALTH CARE OF THE RUSSIAN FEDERATION 69, no. 1 (2025): 65–69. https://doi.org/10.47470/0044-197x-2025-69-1-65-69.

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The nutritional status in an athlete depends on the individual genetic characteristics of the body, the level of physical and psycho-emotional stress, and a balanced diet with the inclusion of specialized food products and dietary supplements. The development of big data analytics and artificial intelligence can contribute to the development of nutritional recommendations at the individual or stratified level. The purpose of the review is to analyze and summarize research papers devoted to the possibilities of using digital technologies, deep machine learning techniques, and artificial intelli

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Senthil, G. A., K. M. Monica, R. Prabha, L. Prinslin, and R. Elavarasi. "Quantum AI: A Cognitive Machine Learning Technique based on Nurturing Food Security Sustainability Predictive Analysis for Life Science - Bioengineering in Healthcare." BIO Web of Conferences 172 (2025): 02002. https://doi.org/10.1051/bioconf/202517202002.

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Individualized and accurate evaluation of nutrient intake is essential for good health. Disease prevention and increased food security This article combines image analysis with quantum algorithms for precise food insights, introducing an advanced quantum-enhanced AI system. It is designed to predict the nutritional content of foods consumed. The system starts with taking photos of food using a Convolutional Neural Network (CNN) is processed by it has a classification accuracy of 91.87%. User-specific information such as age, weight, height, and BMI are also used to calculate individual nutriti

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Gajalakshmi N, Andalpriya C, Raja Lakshmi K, and Nuttrenai V. "Fit AI-Personalized Diet and Fitness Planner." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 03 (2025): 508–13. https://doi.org/10.47392/irjaem.2025.0080.

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Maintaining a healthy lifestyle is becoming increasingly challenging due to hectic schedules, unhealthy eating habits, and the lack of personalized diet and fitness guidance. Generic health plans often fail to address individual requirements, leading to ineffective results and poor adherence. To overcome these challenges, FitAI: Personalized Diet and Fitness Planner is developed as an AI-powered web application that provides customized diet and fitness recommendations based on user-specific data. The system collects key user inputs, including age, height, weight, gender, exercise frequency, di

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B, Bhuvaneshwari. "AI-Driven Smart Food Ordering System with Personalized Nutrition Recommendations using Conversational Interface." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 3108–22. https://doi.org/10.22214/ijraset.2025.68799.

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With the increasing popularity of online food ordering platforms, there remains a significant gap in delivering personalized and health-conscious food recommendations. This paper presents a Smart Food Ordering System that integrates AI-driven personalization based on individual user health data. The proposed system combines natural language processing (NLP) with FastText embeddings for intent classification and chatbot interaction, enabling users to place food orders through a conversational interface. Personalized recommendations are generated by analyzing user-specific health parameters such

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Patra, Elena, Anna Kokkinopoulou, Saskia Wilson-Barnes, et al. "Personal Goals, User Engagement, and Meal Adherence within a Personalised AI-Based Mobile Application for Nutrition and Physical Activity." Life 14, no. 10 (2024): 1238. http://dx.doi.org/10.3390/life14101238.

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Mobile applications have been shown to be an effective and feasible intervention medium for improving healthy food intake in different target groups. As part of the PeRsOnalized nutriTion for hEalthy livINg (PROTEIN) European Union H2020 project, the PROTEIN mobile application was developed as an end-user environment, aiming to facilitate healthier lifestyles through artificial intelligence (AI)-based personalised dietary and physical activity recommendations. Recommendations were generated by an AI advisor for different user groups, combining users’ personal information and preferences with a

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Stefanidis, Kiriakos, Dorothea Tsatsou, Dimitrios Konstantinidis, et al. "PROTEIN AI Advisor: A Knowledge-Based Recommendation Framework Using Expert-Validated Meals for Healthy Diets." Nutrients 14, no. 20 (2022): 4435. https://doi.org/10.3390/nu14204435.

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AI-based software applications for personalized nutrition have recently gained increasing attention to help users follow a healthy lifestyle. In this paper, we present a knowledge-based recommendation framework that exploits an explicit dataset of expert-validated meals to offer highly accurate diet plans spanning across ten user groups of both healthy subjects and participants with health conditions. The proposed advisor is built on a novel architecture that includes (a) a qualitative layer for verifying ingredient appropriateness, and (b) a quantitative layer for synthesizing meal plans. The

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ODEH, Joseph. "Exploring AI Applications to Foster Healthy Shopping Habits in Nigerian Retail." International Journal of Research and Innovation in Social Science VIII, IIIS (2024): 5382–93. https://doi.org/10.47772/ijriss.2024.803403s.

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The increased awareness has opened avenues to explore innovative strategies for promoting sustainable consumer behaviour. Notably, artificial intelligence (AI) has become a transformative tool, redefining how consumers interact with products and make purchasing choices. This paper explored AI applications to foster healthy shopping habits in Nigerian retail. The aim of the study was to determine the relationship between AI applications to foster healthy shopping habits in Nigerian retail. The study adopted the survey research design. Based on the research questions, a structured questionnaire

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Al-Shareeda, Mahmood A., Ahmed Abdulazeez Obaid, and Amjad Abdul Hamid Almajid. "The Role of Artificial Intelligence in Bodybuilding: A Systematic Review of Applications, Challenges, and Future Prospects." Jordanian Journal of Informatics and Computing 2025, no. 1 (2025): 16–26. https://doi.org/10.63180/jjic.thestap.2025.1.3.

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Artificial Intelligence (AI) is making more and more impact on bodybuilding and helps providing data driven insights for improved training, nutrition, performance analytics, injury prevention and supplementation. This article systematically reviews the impact of AI on five key aspects of bodybuilding. For example, the adaptive workout plans and real-time training feedback in AI-Based Training Optimization improve progressive overload and movement accuracy. Second, it is AI-Driven Nutrition & Diet Planning that will refine macronutrient tracking, each meal customization, and genetics-based

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Pardhi, Praful R., Saurabh Wagh, Gaurav Sharma, Abhash Goyal, and Pavan Pawar. "Enhancing Personalized Fitness: Integrating Large Language Model." EPJ Web of Conferences 328 (2025): 01021. https://doi.org/10.1051/epjconf/202532801021.

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This paper explores the integration of Large Language Models (LLMs) into workout planning and personal training to meet the growing demand for personalized fitness solutions. Traditional personal training, while effective, faces challenges in accessibility, scalability, and real-time adaptability. We propose a novel AI-powered approach using LLMs to address these limitations and enhance the training experience. Our methodology combines the natural language processing capabilities of LLMs with exercise science and nutrition principles. The system provides 24/7 personalized guidance, dynamic wor

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Papastratis, Ilias, Andreas Stergioulas, Dimitrios Konstantinidis, Petros Daras, and Kosmas Dimitropoulos. "Can ChatGPT provide appropriate meal plans for NCD patients?" Nutrition 121 (November 11, 2023): 112291. https://doi.org/10.1016/j.nut.2023.112291.

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Dietary habits have a significant impact on health condition and are closely related to the onset and progression of non-communicable diseases. Consequently, a well-balanced diet plays an important role as a treatment to lessen the effects of various disorders, including non-communicable diseases. To propose healthy and nutritious diets, several AI recommendation systems have been developed, with most of them using expert knowledge and guidelines to provide tailored diets and encourage healthier eating habits. On the other hand, new advances on Large Language Models (LLMs) such as ChatGPT, wit

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Huang, Han Chun, and Hsiao Wen Chuang. "A Pilot Study on AI-Powered Gamified Chatbot with OMO Strategy for Enhancing Parental Nutrition Knowledge." Digital 5, no. 2 (2025): 13. https://doi.org/10.3390/digital5020013.

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This pilot study explores the efficacy of an AI-powered gamified chatbot integrated with an Online-Merge-Offline (OMO) strategy to enhance parental nutrition knowledge. Conducted in a Taiwanese public childcare setting, the intervention comprised eight weekly nutrition seminars delivered by registered dietitians, supplemented by a LINE-based chatbot providing interactive, gamified learning experiences. Pre-test and post-test evaluations were administered via the chatbot to assess knowledge acquisition. The results from 20 unique participants, including 9 with complete data, indicated a statist

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Tidake, Prof Ankita, Anurag Amale, Akash Madhavan, Atharva Jadhav, and Soham Waghmare. "Review on WorkFit Balance: A Daily Fitness Scheduler." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 487–90. https://doi.org/10.22214/ijraset.2025.68303.

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Abstract: The increasing demand for personalized fitness solutions has led to the development of technology-driven applications that cater to individual health goals. This paper presents WorkFit Balance, an AI-driven fitness planning application designed to generate personalized daily plans for exercise and nutrition based on user profiles and schedules. Leveraging machine learning techniques, specifically the K-Nearest Neighbors (KNN) algorithm, the application recommends exercises and meals tailored to the user’s fitness goals, fitness type, and body mass index (BMI). The system integrates w

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Schroder, Theresa, Windy Wang, Kelsey Cochrane, et al. "Personalized Nutrition Recommendations Improve Plasma Metabolite Concentrations Related to Dietary Intake." Current Developments in Nutrition 4, Supplement_2 (2020): 1274. http://dx.doi.org/10.1093/cdn/nzaa058_032.

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Abstract Objectives The objectives of our proof-of-concept study was to assess the efficacy of personalized nutrition interventions on diet and chronic disease risk. Methods Fasting plasma samples were collected at day 1 and day 100 of a cohort of 148 adults (aged 23–65y) volunteers with a median (range) BMI of 25.8 (17.2–48.3). At both time points 119 metabolites were quantitated using LC-MS/MS. Based on their metabolite concentrations and dietary preferences, each participant received their own personalized nutrition recommendations through an AI-assisted online platform and were advised to

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Mrunal, Patil, and Pamul Akshaya. "“FemiSync” Your Personal Companion using Cross Platform Framework and AI." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44501.

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The FemiSync - Your Personal Companion is a mobile-based solution designed to assist individuals in tracking their menstrual cycles, receiving timely notifications, and accessing personalized health recommendations. The application features an intuitive period tracker, reminders for upcoming cycles, ovulation predictions, and symptom logging to enhance self-care. Additionally, it offers tailored exercise routines and wellness tips to alleviate menstrual discomfort and promote overall well-being. With a user-friendly interface, privacy-focused data handling, and AI-driven insights, the app aims

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Kusuma, Joyce D., Hsiao-Ling Yang, Ya-Ling Yang, Zhao-Feng Chen, and Shyang-Yun Pamela Koong Shiao. "Validating Accuracy of a Mobile Application against Food Frequency Questionnaire on Key Nutrients with Modern Diets for mHealth Era." Nutrients 14, no. 3 (2022): 537. http://dx.doi.org/10.3390/nu14030537.

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In preparation for personalized nutrition, an accurate assessment of dietary intakes on key essential nutrients using smartphones can help promote health and reduce health risks across vulnerable populations. We, therefore, validated the accuracy of a mobile application (app) against Food Frequency Questionnaire (FFQ) using artificial intelligence (AI) machine-learning-based analytics, assessing key macro- and micro-nutrients across various modern diets. We first used Bland and Altman analysis to identify and visualize the differences between the two measures. We then applied AI-based analytic

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I.M.D.J.R. B, Ilukpitiya, Herath H.M.R. B, Rajakaruna R.H.M.S.A, Herath M.H.S.M, Koliya Pulasinghe, and Jenny Krishara. "AI-Driven Personalized Fitness Coaching with Body Type-Based Workout and Nutrition Plans and Real-Time Exercise Feedback." International Journal of Preventive Medicine and Health 5, no. 1 (2024): 17–23. https://doi.org/10.54105/ijpmh.e8176.05011124.

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In today’s fast-paced world filled with distractions such as work, family, education and other commitments, people often have little time and energy to maintain a healthy lifestyle. Traditional fitness approaches frequently fail to meet the dynamic needs and expectations of modern users, leading to dissatisfaction and disengagement. A main problem of traditional systems is the lack of personalization for diverse users, that also monitors and tracks the workout progression. This study introduces an innovative system that creates a personalized workout and diet plan which aims to engage and moti

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Chandrashekhar Patil, Rahul. "NUTRIFLEX." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (2024): 1–6. http://dx.doi.org/10.55041/ijsrem37947.

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The NUTRIFLEX project is a comprehensive AI-powered application designed to provide personalized nutrition and health management solutions. The goal of the project is to assist individuals in optimizing their diet based on their unique biological and emotional needs, including gut health, mood, and overall well-being. By combining artificial intelligence (AI) and user input, NUTRIFLEX offers tailored meal plans, calorie predictions, mood-enhancing food recommendations, and a gamified approach to motivate users to maintain a healthy lifestyle. The problem addressed by NUTRIFLEX is the growing c

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Sobol, Żaneta, Rafał Chiczewski, and Dorota Wątróbska-Świetlikowska. "The Modern Approach to Total Parenteral Nutrition: Multidirectional Therapy Perspectives with a Focus on the Physicochemical Stability of the Lipid Fraction." Nutrients 17, no. 5 (2025): 846. https://doi.org/10.3390/nu17050846.

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With advancements in medical technology, biochemistry, and clinical practices, the modern approach to total parenteral nutrition (TPN) has been focused on precision, safety, and the optimization of metabolic and nutritional parameters based on the patient’s needs. In the last decade, TPN mixtures have been transitioning from a lifesaving intervention for patients unable to receive enteral nutrition to a highly specialized therapy aimed at improving clinical outcomes, reducing complications, and personalizing care. Total parenteral nutrition has attracted great interest, and its adaptation to t

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Stefanidis, Kiriakos, Dorothea Tsatsou, Dimitrios Konstantinidis, et al. "PROTEIN AI Advisor: A Knowledge-Based Recommendation Framework Using Expert-Validated Meals for Healthy Diets." Nutrients 14, no. 20 (2022): 4435. http://dx.doi.org/10.3390/nu14204435.

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AI-based software applications for personalized nutrition have recently gained increasing attention to help users follow a healthy lifestyle. In this paper, we present a knowledge-based recommendation framework that exploits an explicit dataset of expert-validated meals to offer highly accurate diet plans spanning across ten user groups of both healthy subjects and participants with health conditions. The proposed advisor is built on a novel architecture that includes (a) a qualitative layer for verifying ingredient appropriateness, and (b) a quantitative layer for synthesizing meal plans. The

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Lucassen, Desiree A., Marlou P. Lasschuijt, Guido Camps, et al. "Short and Long-Term Innovations on Dietary Behavior Assessment and Coaching: Present Efforts and Vision of the Pride and Prejudice Consortium." International Journal of Environmental Research and Public Health 18, no. 15 (2021): 7877. http://dx.doi.org/10.3390/ijerph18157877.

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Overweight, obesity and cardiometabolic diseases are major global health concerns. Lifestyle factors, including diet, have been acknowledged to play a key role in the solution of these health risks. However, as shown by numerous studies, and in clinical practice, it is extremely challenging to quantify dietary behaviors as well as influencing them via dietary interventions. As shown by the limited success of ‘one-size-fits-all’ nutritional campaigns catered to an entire population or subpopulation, the need for more personalized coaching approaches is evident. New technology-based innovations

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Oluwemimo Adetunji, Patrick Tamarauefiye Evah, and Ifeanyichukwu Ezeanyagu. "Developing personalized diabetes management plans using artificial intelligence and machine learning." World Journal of Advanced Research and Reviews 13, no. 2 (2022): 605–28. https://doi.org/10.30574/wjarr.2022.13.2.0136.

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Diabetes is a lifelong condition which is associated with abnormally high blood sugar levels due to insufficient production of insulin or failure of the body to use the hormone efficiently. Due to a rising number of diabetes cases globally, the need to develop efficient and targeted approaches for the disease is more pressing than pre/application. Artificial intelligence (AI) and machine learning (ML) are among the most significant innovations in the healthcare industry, creating new positive directions for creating individualized plans for diabetes mellitus management. AI and ML are not just

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Kemothi, Swati, Santosh Singh, and Pooja Varma. "Personalized Medical Diet Recommendations for Disease Management and Improved Patient Outcomes." Seminars in Medical Writing and Education 2 (December 30, 2023): 127. https://doi.org/10.56294/mw2023127.

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Personalized health diets play a crucial role in infection management by tailoring diet recommendation systems to routine data, genetic factors, and specific medical conditions. Research introduces the Intelligent Nutcracker Optimized Effective Decision Tree (INO-EDT) model, designed to provide individualized nutritional guidance for managing chronic illnesses, particularly diabetes and heart disease. Medical files, questionnaires, wearable devices, and food journals serve as sources of patient data standardization and cleaning to ensure accuracy and stability. Machine Learning (ML) techniques

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Rojanaphan, Pakapon. "Automated Nutrient Deficiency Detection and Recommendation Systems Using Deep Learning in Nutrition Science." International Journal of Scientific Research and Management (IJSRM) 12, no. 11 (2024): 1746–63. http://dx.doi.org/10.18535/ijsrm/v12i11.ec09.

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Nutrient deficiencies affect millions globally, contributing to severe health issues and reduced quality of life. Traditional methods of diagnosing these deficiencies and recommending dietary adjustments are often time-intensive, prone to error, and lack personalization. The advent of deep learning has revolutionized nutrition science, offering automated, accurate, and scalable solutions. This paper delves into the development and application of automated nutrient deficiency detection and recommendation systems powered by deep learning. Key components of such systems include advanced data proc

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Wang, Yawen, Yan Wen, Xiaofeng Wu, Lilu Wang, and Hongwei Cai. "Assessing the Role of Adaptive Digital Platforms in Personalized Nutrition and Chronic Disease Management." World Journal of Innovation and Modern Technology 8, no. 1 (2025): 24–31. https://doi.org/10.53469/wjimt.2025.08(01).05.

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Digital platforms have increasingly been utilized for personalized nutrition interventions in the management of chronic diseases. This meta-analysis evaluates their efficacy by synthesizing data from 55 studies involving 16,280 participants, focusing on three critical outcomes: weight management, glycemic control, and cardiovascular health. AI-based platforms demonstrated significant clinical improvements, achieving an average weight reduction of 4.6 kg, an HbA1c decrease of 1.1%, and an LDL cholesterol reduction of 18.6 mg/dL. Wearable-integrated systems delivered even greater outcomes in som

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Taneja, Akriti, Gayathri Nair, Manisha Joshi, et al. "Artificial Intelligence: Implications for the Agri-Food Sector." Agronomy 13, no. 5 (2023): 1397. http://dx.doi.org/10.3390/agronomy13051397.

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Artificial intelligence (AI) involves the development of algorithms and computational models that enable machines to process and analyze large amounts of data, identify patterns and relationships, and make predictions or decisions based on that analysis. AI has become increasingly pervasive across a wide range of industries and sectors, with healthcare, finance, transportation, manufacturing, retail, education, and agriculture are a few examples to mention. As AI technology continues to advance, it is expected to have an even greater impact on industries in the future. For instance, AI is bein

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McTear, Michael, Kristiina Jokinen, Sonja Dana Roelen, et al. "Streamlining Sensor Technology: Focusing on Data Fusion and Emotion Evaluation in the e-VITA Project." Sensors 25, no. 7 (2025): 2217. https://doi.org/10.3390/s25072217.

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This paper explores the use of sensor-based multimodal data fusion and emotion detection technologies in e-VITA, a three-year EU–Japan collaborative project that developed an AI-powered virtual coaching system to support independent living for older adults. The system integrates these technologies to enable individualized profiling and personalized recommendations across multiple domains, including nutrition, physical exercise, sleep, cognition, spirituality, and social health. Following a review of related work, we detail the implementation and evaluation of data fusion and emotion detection

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H S, Naveen. "Smart Dietary Planning with Food Recognition and Calorie intake Estimation." International Journal of Innovative Research in Advanced Engineering 12, no. 04 (2025): 150–60. https://doi.org/10.26562/ijirae.2025.v1204.05.

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The escalating prevalence of lifestyle-related health issues, such as obesity and diabetes, highlights the critical importance of health awareness and effective lifestyle management. Advanced dietary tracking solutions have emerged as essential tools to address these challenges. This paper presents an innovative AI-driven system that automates food recognition, calorie estimation, and personalized meal recommendations using cutting-edge machine learning and image processing techniques. Leveraging deep learning models, including Convolutional Neural Networks (CNNs), SIFT, and Gabor filters, the

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Iapăscurtă, Victor, Dinu Țurcanu, and Rodica Siminiuc. "Integration of a data-driven software application and a multimodal large language model for enhanced nutritional guidance: a case study." Journal of Engineering Science 31, no. 3 (2024): 75–84. https://doi.org/10.52326/jes.utm.2024.31(3).07.

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In the realm of health and wellness, the integration of data-driven technology and artificial intelligence (AI) has opened up new possibilities for personalized and data-driven approaches. HN-Assistant, a software application designed to analyze an individual's nutritional state and provide tailored recommendations, offers a powerful tool for promoting healthy eating habits. The HN-Assistant can also analyze how good a food product is at covering the estimated nutrient requirements. However, when combined with the capabilities of advanced AI assistants based on LLMs, the potential for comprehe

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Liu, Mingzhe, and Xia Liu. "Molecular and cellular level analysis of the mechanism of nutritional intervention in preventing epidemic virus infection." Molecular & Cellular Biomechanics 21 (September 3, 2024): 129. http://dx.doi.org/10.62617/mcb.v21.129.

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Molecular nutrition encompasses a wider range of investigations than nutritional genomics, which can be considered a scientific investigation of how different nutrients and dietary components influence the cellular and molecular mechanisms of the body and health. In effect, increasing antioxidant elements in daily diets can assist with combating the inflammatory response caused by cytokines in the human body. Antioxidants can impede the oxidation process and hence avoid the creation of free radicals in the cytoplasm that can damage the cells via chain reactions. Contamination sequences, batch

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Nursiah, Andi. "The Role of Digital Technology in Improving Nutrition Education for Millennials and Generation Z." Journal Nutrizione 1, no. 3 (2024): 11–20. https://doi.org/10.62872/s85wek63.

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Digital technology has brought about a revolution in personalized nutrition for Millennials and Z generations, who increasingly rely on artificial intelligence (AI)-based apps for dietary recommendations tailored to individual needs. These apps analyze personal data such as weight, height, physical activity and food preferences to provide more relevant advice. Devices such as smartwatches also help track diet and healthy living habits. However, challenges related to data privacy and security are still major issues that require strict regulation. On the other hand, digital technology is also ex

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Trajkovska Petkoska, Anka, Violeta Ognenoska, and Anita Trajkovska-Broach. "Mediterranean Diet: From Ancient Traditions to Modern Science—A Sustainable Way Towards Better Health, Wellness, Longevity, and Personalized Nutrition." Sustainability 17, no. 9 (2025): 4187. https://doi.org/10.3390/su17094187.

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The Mediterranean Diet (MD), although not always called by this name, has emerged over centuries as a diet influenced by diverse civilizations in the Mediterranean region, who blended local produce, traditions, and rituals with new ingredients and customs introduced through trade, migrations, or occupations. Historically characterized mainly by plant-based foods, olive oil, fish, moderate meat consumption, and moderate wine consumption, MD was also shaped by the holistic health principles advocated by figures like Hippocrates, Plato and Galen. Modern investigations, including Ancel Keys’ Seven

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Zarkogianni, Konstantia, Evi Chatzidaki, Nektaria Polychronaki, et al. "The ENDORSE Feasibility Study: Exploring the Use of M-Health, Artificial Intelligence and Serious Games for the Management of Childhood Obesity." Nutrients 15, no. 6 (2023): 1451. http://dx.doi.org/10.3390/nu15061451.

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Childhood obesity constitutes a major risk factor for future adverse health conditions. Multicomponent parent–child interventions are considered effective in controlling weight. Τhe ENDORSE platform utilizes m-health technologies, Artificial Intelligence (AI), and serious games (SG) toward the creation of an innovative software ecosystem connecting healthcare professionals, children, and their parents in order to deliver coordinated services to combat childhood obesity. It consists of activity trackers, a mobile SG for children, and mobile apps for parents and healthcare professionals. The het

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