Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review

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The Age of Artificial Intelligence: Use of Digital Technology in Clinical Nutrition

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Artificial intelligence & clinical nutrition: What the future might have in store

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The Application of Digital Technologies and Artificial Intelligence in Healthcare: An Overview on Nutrition Assessment

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From Reflection to Action: Combining Machine Learning with Expert Knowledge for Nutrition Goal Recommendations

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Using Natural Language Processing and Artificial Intelligence to Explore the Nutrition and Sustainability of Recipes and Food

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Applicability of machine learning techniques in food intake assessment: A systematic review

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Chronic Kidney Disease Prediction and Recommendation of Suitable Diet Plan by using Machine Learning

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Classification and Prediction on the Effects of Nutritional Intake on Overweight/Obesity, Dyslipidemia, Hypertension and Type 2 Diabetes Mellitus Using Deep Learning Model: 4–7th Korea National Health and Nutrition Examination Survey

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A DASH Diet Recommendation System for Hypertensive Patients Using Machine Learning

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Deep Learning-Based Near-Infrared Hyperspectral Imaging for Food Nutrition Estimation

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Digital Anti-Aging Healthcare: An Overview of the Applications of Digital Technologies in Diet Management

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Predictions of Diabetes and Diet Recommendation System for Diabetic Patients using Machine Learning Techniques

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Validation of Mobile Artificial Intelligence Technology–Assisted Dietary Assessment Tool Against Weighed Records and 24-Hour Recall in Adolescent Females in Ghana

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The future of artificial intelligence in clinical nutrition

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Artificial Intelligence Technology for Food Nutrition

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Application of the deep learning algorithm in nutrition research – using serum pyridoxal 5′-phosphate as an example

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Diet Recommendation System for Human Health Using Machine Learning

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Personalized Nutrition: Tailoring Dietary Recommendations through Genetic Insights

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Investigation and Assessment of AI’s Role in Nutrition—An Updated Narrative Review of the Evidence

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Digital Cardiovascular Twins, AI Agents, and Sensor Data: A Narrative Review from System Architecture to Proactive Heart Health

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A Review of Deep Learning Techniques for Leukemia Cancer Classification Based on Blood Smear Images

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Artificial Intelligence in Nutrition and Dietetics: A Comprehensive Review of Current Research

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The Influence of an AI-Driven Personalized Nutrition Program on the Human Gut Microbiome and Its Health Implications

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AI-Powered Innovations in Food Safety from Farm to Fork

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AI‐Driven Personalized Nutrition: Integrating Omics, Ethics, and Digital Health

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Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review

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Artificial Intelligence in the Management of Malnutrition in Cancer Patients: A Systematic Review

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Health is beyond genetics: on the integration of lifestyle and environment in real-time for hyper-personalized medicine

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An Explainable CNN and Vision Transformer-Based Approach for Real-Time Food Recognition

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Unraveling MASLD: The Role of Gut Microbiota, Dietary Modulation, and AI-Driven Lifestyle Interventions

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Food and Medicine Homology Focus in 2026

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Harnessing Artificial Intelligence to Safeguard Food Quality and Safety

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AI-Driven Real-Time Classification of ECG Signals for Cardiac Monitoring Using i-AlexNet Architecture

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Assessing the Links Between Artificial Intelligence and Precision Nutrition

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Exploring the impact of bioactive peptides from fermented Milk proteins: A review with emphasis on health implications and artificial intelligence integration

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Data Ownership in the AI-Powered Integrative Health Care Landscape

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Application of Deep Learning Technology in Monitoring Plant Attribute Changes

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Harnessing Artificial Intelligence and Precision Diets for Brain Health and Cognitive Resilience

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The multiple uses of artificial intelligence in exercise programs: a narrative review

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Comprehending the Epidemiology and Aetiology of Childhood Obesity: Integrating Life Course Approaches for Prevention and Intervention

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Optimizing Nutritional Outcomes: The Role of AI in Personalized Diet Planning

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Impact of Artificial Intelligence in Nursing for Geriatric Clinical Care for Chronic Diseases: A Systematic Literature Review

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Precision nutrition: Is tailor‑made dietary intervention a reality yet? (Review)

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The convergence of food science and nutrigenomics: Exploring new frontiers in innovation and development opportunities

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Improving Personalized Meal Planning with Large Language Models: Identifying and Decomposing Compound Ingredients

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The Impact of Artificial Intelligence Technologies on Nutritional Care in Patients with Chronic Kidney Disease: A Systematic Review.

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Alzheimer’s Multiclassification Using Explainable AI Techniques

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Perspective: Multiomics and Artificial Intelligence for Personalized Nutritional Management of Diabetes in Patients Undergoing Peritoneal Dialysis

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Can artificial intelligence (AI) chatbot tools be used effectively for nutritional management in obesity?

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Perspectives, challenges and future of artificial intelligence in personalised nutrition research

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The role of diet in cancer: the potential of shaping public policy and clinical outcomes in the UK

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Machine learning-based predictive model for enteral nutrition-associated diarrhea in ICU patients and its nursing applications

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The Use of Artificial Intelligence (AI) to Support Dietetic Practice Across Primary Care: A Scoping Review of the Literature

Ngo, K., Mekhail, S., Chan, V., Li, X., Yin, A., Choi, H. Y., Allman-Farinelli, M. A., & Chen, J. (2025) · Nutrients · doi:10.3390/nu17223515 · Cited by 14 · see citing works

Enhancing dietary analysis: Using machine learning for food caloric and health risk assessment

Adjuik, T. A., Boi‐Dsane, N. A. A., & Kehinde, B. A. (2024) · Journal of Food Science · doi:10.1111/1750-3841.17421 · Cited by 12 · see citing works

Deep learning-assisted screening and diagnosis of scoliosis: segmentation of bare-back images via an attention-enhanced convolutional neural network

Duan, X., Ma, X., Zhu, M., Wang, L., You, D., Deng, L., & Niu, N. (2025) · Journal of Orthopaedic Surgery and Research · doi:10.1186/s13018-025-05564-y · Cited by 12 · see citing works

Artificial Intelligence in Clinical Nutrition: Bridging Data Analytics and Nutritional Care

Varayil, J. E., Bielinski, S. J., Mundi, M. S., Bonnes, S. L. R., Salonen, B. R., & Hurt, R. T. (2025) · Current Nutrition Reports · doi:10.1007/s13668-025-00680-x · Cited by 15 · see citing works

Application of Artificial Intelligence Technologies as an Intervention for Promoting Healthy Eating and Nutrition in Older Adults: A Systematic Literature Review

Kalu, K. A., Ataguba, G., Onifade, O. M., Orji, F. A., Giweli, N., & Orji, R. (2025) · Nutrients · doi:10.3390/nu17203223 · Cited by 12 · see citing works

Precision nutrition through diet-gut microbiome interactions: Emerging insights driven by artificial intelligence, microbiome health metrics, and mechanistic modeling

Barrera-Suarez, M. A., Zhao, C., Karnatovskaia, L. V., & Sung, J. (2026) · Gut Microbes Reports · doi:10.1080/29933935.2026.2650247 · Cited by 11 · see citing works

Artificial Intelligence for Dietary Management

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Good practices and common pitfalls of machine learning in nutrition research

Kirk, D. (2024) · Proceedings of The Nutrition Society · doi:10.1017/s0029665124007638 · Cited by 11 · see citing works

Artificial intelligence in drug development for delirium and Alzheimer’s disease

Ai, R., Xiao, X., Deng, S., Yang, N., Xing, X., Watne, L. O., Selbæk, G., Wedatilake, Y., Xie, C., Rubinsztein, D. C., et al. (2025) · Acta Pharmaceutica Sinica B · doi:10.1016/j.apsb.2025.04.026 · Cited by 11 · see citing works

Ingredient-Guided RGB-D Fusion Network for Nutritional Assessment

Feng, Z., Xiong, H., Min, W., Hou, S., Duan, H., Liu, Z., & Jiang, S. (2024) · IEEE Transactions on AgriFood Electronics · doi:10.1109/tafe.2024.3493332 · Cited by 10 · see citing works

A knowledge-based decision support system to support family doctors in personalizing type-2 diabetes mellitus medical nutrition therapy

Spoladore, D., Stella, F., Tosi, M., Lorenzini, E. C., & Bettini, C. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109001 · Cited by 10 · see citing works

Process Optimization for Antarctic Krill (Euphausia superba) Sauce Based on Back Propagation Neural Network Combined with Genetic Algorithm

Chi, H., Fu, Z., Wang, P., Yu, D., Zhao, L., Li, L., Liu, Y., & Zheng, J. (2024) · Applied Sciences · doi:10.3390/app14167337 · Cited by 9 · see citing works

Artificial intelligence and precision nutrition: tailoring diets with data-driven insights

Sharma, R., Gaur, S. S., Ajay, A., Shams, R., & Dash, K. K. (2025) · Nutrire · doi:10.1186/s41110-025-00360-4 · Cited by 11 · see citing works

Intelligent diet recommendation system powered by artificial intelligence for personalized nutritional solutions

Amadeh, T., Rafie, M., Radmanesh, S., Azizi, A., Ahangarian, A., Fathollahi, P., & Ahmadloo, H. (2025) · Clinical Nutrition ESPEN · doi:10.1016/j.clnesp.2025.09.002 · Cited by 10 · see citing works

Artificial intelligence in clinical nutrition. A narrative review

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From Traditional Medical Patterns to Artificial Intelligence: The Applicability of Digital Biomarkers in Reinventing Personalised Nutrition

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The role of artificial intelligence in precision exercise nutrition: a shift from data to diets

Nasb, M., Zhang, Y., & Chen, N. (2025) · Food Science and Human Wellness · doi:10.26599/fshw.2025.9250623 · Cited by 7 · see citing works

Recent advances in implicit salt reduction innovative technologies: Innovative strategies and future challenges

Wei, Z., Li, S., Jiang, Y., & Shi, J. (2025) · Food Research International · doi:10.1016/j.foodres.2025.117346 · Cited by 7 · see citing works

Smartphones as Catalysts for Synergistic Nutrition: A New Era in Bioactive Detection, Personalization, and Food System Intelligence

Younis, M. I., Sallam, Y. I., Mahmoud, K. F., Ruan, Z., Tlay, R. H., & Abedelmaksoud, T. G. (2025) · Food Science & Nutrition · doi:10.1002/fsn3.70880 · Cited by 7 · see citing works

Study protocol for design of a personalized dietary supplement based on the gut microbiota of Alzheimer’s patients and evaluation of its effects in a pilot randomized controlled trial

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Industry 4.0 technologies promote healthy and nutritious food

Hassoun, A., & Galanakis, C. M. (2025) · Discover Food · doi:10.1007/s44187-025-00639-5 · Cited by 13 · see citing works

Biosynthesis of high-value chorismate derivatives in Escherichia coli: Recent advances and perspectives

Pan, X., Jiang, S., Tang, M., Wang, N., Sun, Q., Yang, T., & Rao, Z. (2025) · Biotechnology Advances · doi:10.1016/j.biotechadv.2025.108762 · Cited by 8 · see citing works

Comparison of ChatGPT-3.5, ChatGPT-4.0 and DeepSeek in generating dietary plans for patients with chronic kidney disease: A focus on nutritional accuracy and dietary inflammation

You, Q., Zhou, L., Ma, Y., Guo, J., Wang, Y., Shi, L., Deng, Y., Rao, Z., & Li, X. (2025) · Nutrition · doi:10.1016/j.nut.2025.112957 · Cited by 6 · see citing works

Benchmarking ChatGPT and Other Large Language Models for Personalized Stage-Specific Dietary Recommendations in Chronic Kidney Disease

Kairat, M., Adilmetova, G., Ibraimova, I., Gaipov, A., Varol, H. A., & Chan, M. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14228033 · Cited by 6 · see citing works

Multidisciplinary prediction of running-related injuries using machine learning

Wu, H., Brooke-Wavell, K. S. F., Barnes, M. R., Awan, Z., Mastana, S. S., Allen, S., & Blagrove, R. C. (2026) · npj Digital Medicine · doi:10.1038/s41746-026-02413-y · Cited by 6 · see citing works

Integration of metabolomics and machine learning for precise management and prevention of cardiometabolic risk in Asians

Bi, X., Sun, L., Yeo, M. T. Y., Seaw, K. M., & Leow, M. K. (2025) · Clinical Nutrition · doi:10.1016/j.clnu.2025.05.011 · Cited by 5 · see citing works

Harnessing machine learning for improved diagnosis, drug discovery, and patient care

Paul, J. K., Azmal, M., Talukder, O. F., Haque, A. S. N. B., Meem, M., & Ghosh, A. (2025) · Computational and Structural Biotechnology Reports · doi:10.1016/j.csbr.2025.100051 · Cited by 8 · see citing works

Integrated Medical and Digital Approaches to Enhance Post-Bariatric Surgery Care: A Prototype-Based Evaluation of the NutriMonitCare System in a Controlled Setting

Marin, R., Ianculescu, M., Costescu, M., Mocanu, V., Mihăescu, A.-G., Fulga, I., & Coman, O. (2025) · Nutrients · doi:10.3390/nu17152542 · Cited by 4 · see citing works

Precision Nutritional Genomics, Gut Microbiota and Artificial Intelligence in Chronic Kidney Disease

Mahdavi, S. (2025) · Journal of the American Nutrition Association · doi:10.1080/27697061.2025.2549893 · Cited by 5 · see citing works

Artificial intelligence applications in nutrition science: A brief thematic overview

Bíró, L. (2025) · Acta Alimentaria · doi:10.1556/066.2025.00210 · Cited by 5 · see citing works

Bridging Distance, Delivering Care: Pediatric Tele-Nutrition in the Digital Health Era—A Narrative Review

Haimi, M., & Inchi, L. (2025) · Healthcare · doi:10.3390/healthcare13233107 · Cited by 5 · see citing works

Synergistic Integration of Artificial Intelligence, Organoid Models, and Multi‐Omics Technologies in Contemporary Drug Discovery

Dinakarkumar, Y., Ganesan, S. K., John, R. M., Raj, A. L. T., Raja, H., Selvaraj, A., Punitha, N., & Venkatesh, A. (2025) · Advanced Therapeutics · doi:10.1002/adtp.202500532 · Cited by 5 · see citing works

On the Possibility of Using Artificial Intelligence in Medicine: From Theory to Practice

Николаевич, Т. В., Полунина, Е. Г., Kurenkov, V. V., Trubilin, А. V., Владимирович, К. Е., Kasparova, E. A., Arabadzhyan, S. I., Filonenko, A. V., Ponomareva, E. N., & Tsaregorodtseva, M. A. (2025) · Ophthalmology in Russia · doi:10.18008/1816-5095-2025-4-725-731 · Cited by 5 · see citing works

Advances and opportunities in measuring dietary intake: from omics to AI

Cohen, Y., Jansen, T., Onwuka, S., & Elinav, E. (2026) · Nature Metabolism · doi:10.1038/s42255-026-01494-z · Cited by 5 · see citing works

Molecular basis of precision nutrition: Food components, microbiome-derived metabolites, and multi-omics modeling

Rahimah, S., Tallei, T. E., Savitri, M., Yamada, C., Kim, H. J., CHOI, M. H., Park, M. N., Ophinni, Y., & Kim, B. (2026) · Food Chemistry Molecular Sciences · doi:10.1016/j.fochms.2026.100411 · Cited by 5 · see citing works

Exploring Trends and Future Developments in the Application of Artificial Intelligence in Food Processing and Preservation

Hussein, J. B., Workneh, T. S., Kassim, A., Mugodo, K., Melesse, S. F., & El‐Mesery, H. S. (2026) · Food Engineering Reviews · doi:10.1007/s12393-026-09445-w · Cited by 6 · see citing works

AI-Powered Analysis of Weight Loss Reports from Reddit: Unlocking Social Media’s Potential in Dietary Assessment

Kaloudis, E., Kouti, V., Triantafillou, F.-M., Ventouris, P., Pavlidis, R., & Bountziouka, V. P. (2025) · Nutrients · doi:10.3390/nu17050818 · Cited by 6 · see citing works

A Comparative Analysis of Interpretable Deep Learning Models for Nutrient Analysis in Vulnerable Populations

Revesai, Z., & Kogeda, O. P. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-96997-3_14 · Cited by 5 · see citing works

Role of artificial intelligence in predicting disease-related malnutrition - A narrative review

Román, D. D. L., Gómez, J. J. L., Galindo, D. E. B., & García, C. G. (2025) · Nutrición Hospitalaria · doi:10.20960/nh.05672 · Cited by 5 · see citing works

Dietary antioxidants and CKM–depression comorbidity: a primary analysis with a secondary evaluation of all-cause mortality using six machine learning algorithms

Tang, Y., Yue, Z., Wu, J., Liu, H., Liu, Q., Zhang, K., Zhang, Y., Wang, Y., Wang, S., Chen, X., et al. (2025) · European journal of medical research · doi:10.1186/s40001-025-03524-0 · Cited by 4 · see citing works

Microbe–Diet Interactions and Personalized Nutrition

Kujawska, M., & Hall, L. J. (2025) · Annual Review of Food Science and Technology · doi:10.1146/annurev-food-111523-121845 · Cited by 4 · see citing works

Comparing ChatGPT and DeepSeek for ultra-processed food classification: AI models for nutritional research and dietary assessment

Cattem, M. V. D. O., Machado, F. O., Silva, M. M., Canella, D. S., & Koury, J. C. (2025) · Nutrition · doi:10.1016/j.nut.2025.113066 · Cited by 5 · see citing works

Guideline Compliance of Artificial Intelligence–Generated Diet Plans After Bariatric Surgery: A Cross-Sectional Simulation Comparing ChatGPT-4o, DeepSeek and Grok-3

Yilmaz, A. B., Kenger, E. B., Karahan, T. Ö., Sağlam, D., & Bas, M. (2025) · Nutrients · doi:10.3390/nu17243957 · Cited by 4 · see citing works

Digitally nourished: unveiling AI solutions for promoting sustainable food consumption – toward a conceptual framework

Rogala, A., & Lučić, A. (2025) · British Food Journal · doi:10.1108/bfj-05-2025-0740 · Cited by 6 · see citing works

Artificial intelligence applications for assessing ultra-processed food consumption: a scoping review

Campbell, J. L., Schofield, G., Tiedt, H. R., & Zinn, C. (2025) · British Journal Of Nutrition · doi:10.1017/s000711452510593x · Cited by 5 · see citing works

Leveraging Deep Learning for Nutritional Profiling: A Comparative Analysis of EfficientNetB0, MobileNetV2, and ResNet50 for Healthy and Unhealthy Food Classification

Motghare, M., Joshi, V. K., Jamal, M., Shanmugam, M., Patil, R. V., & Rawool, N. J. (2025) · IEEE International Conference on Recent Advances in Computing and Systems (REACS) · doi:10.1109/reacs67479.2025.11413325 · Cited by 4 · see citing works

Artificial intelligence in public health—challenges and opportunities

Raj, T. D. S., Philip, A., & Thomas, V. (2026) · European Journal of Clinical Nutrition · doi:10.1038/s41430-026-01745-y · Cited by 5 · see citing works

Artificial intelligence in quality- and safety-driven food formulation: Current applications and future potential

Sanli, I., Özkan, G., & Çapanoğlu, E. (2026) · Food Control · doi:10.1016/j.foodcont.2026.112355 · Cited by 4 · see citing works

Evaluating User Experiences and Preferred Features of a Web-Based 24-Hour Dietary Assessment Tool: Usability Study

Follong, B., Haliburton, C., Mackay, S., Maiquez, M., Grey, J., & Mhurchú, C. N. (2024) · JMIR Formative Research · doi:10.2196/63823 · Cited by 4 · see citing works

Precision Nutrition: Leveraging Machine Learning for Personalized Dietary Recommendations and Health Outcomes

Rojanaphan, P. (2024) · International Journal of Scientific Research and Management (IJSRM) · doi:10.18535/ijsrm/v12i11.ec08 · Cited by 4 · see citing works

A novel paradigm on data and knowledge-driven drug formulation development: Opportunities and challenges of machine learning

Wang, X., Liu, Z., Lin, X., Hong, Y., Shen, L., & Zhao, L. (2025) · Journal of Industrial Information Integration · doi:10.1016/j.jii.2025.100796 · Cited by 4 · see citing works

Machine Learning for Discriminating Microcytic Hypochromic Anemia Based on Erythrocyte Parameters

Lv, J., Li, J., Ren, X., Huang, Q. Y., & Deng, S. (2025) · International Journal of Laboratory Hematology · doi:10.1111/ijlh.14524 · Cited by 5 · see citing works

IDH Mutation Classification in Nonenhancing Gliomas: A Comparison of Habitat and Whole‐Tumor Transfer Learning Strategies

Han, Y., Wang, Y., Cui, W., Xiu, S.-J., Yang, Y., & Zhang, J. (2025) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.70187 · Cited by 4 · see citing works

Nutrition Recommendation System for Sports Persons using Random Forest Algorithm

Vaishnavi, S., Thanujashree., C., Aishwarya, J. S., & Nandhini, N. (2024) · International Conference on Smart Systems for Electrical, Electronics, Communication and Computer Engineering (ICSSEECC) · doi:10.1109/icsseecc61126.2024.10649420 · Cited by 4 · see citing works

Self-Explaining Neural Networks for Food Recognition and Dietary Analysis

Revesai, Z., & Kogeda, O. P. (2025) · BioMedInformatics · doi:10.3390/biomedinformatics5030036 · Cited by 4 · see citing works

Artificial intelligence in nutrition and ageing research – A primer on the benefits

Grootswagers, P., & Grootswagers, T. (2025) · Maturitas · doi:10.1016/j.maturitas.2025.108662 · Cited by 5 · see citing works

Trustworthy Data Science Framework for Non-Invasive Nutritional Screening Using Computer Vision

Hiskiawan, P., Geasela, Y. M., Heryanto, H., Stephanie, E., Ardianti, M., & Sukarno, F. A. (2025) · International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS) · doi:10.1109/icimcis68501.2025.11327268 · Cited by 3 · see citing works

Exploring the broad spectrum of machine learning technologies in the food sector: A comprehensive review of techniques and practical applications

Feng, J., Zheng, F., Zhang, Y., Zeng, X., Wang, Z., Liang, J., Wang, Z., Ji, L., & Shen, Q. (2026) · Current Research in Food Science · doi:10.1016/j.crfs.2026.101406 · Cited by 3 · see citing works

Implementation and Applications of Artificial Intelligence in Nutrition: A Systematic Review of Use in Practice and Research

Vásquez-García, C. F., Tejero, M. E., Naranjo-Martínez, M., & Zagorin-Djaddah, A. (2026) · Nutrients · doi:10.3390/nu18091340 · Cited by 3 · see citing works

Demographic and Physical Determinants of Unhealthy Food Consumption in Polish Long-Term Care Facilities

Ase, A., Borowicz, J., Rakocy, K., Krzych‐Fałta, E., & Samolinski, B. K. (2025) · Nutrients · doi:10.3390/nu17061008 · Cited by 3 · see citing works

Enhancing Dietary Guidance with Machine Learning: A Stacking Classifier Approach to Personalized Nutrition

Abdelmageed, L. N., Fathy, B. E., Ali, R. E., & Borham, M. L. (2024) · International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) · doi:10.1109/miucc62295.2024.10783581 · Cited by 3 · see citing works

Artificial Intelligence in Nutrition: A Comprehensive Review of Applications and Challenges

Bhavsar, J. P., Khara, S. V., & Dave, J. A. (2025) · doi:10.1109/iceti4t63625.2025.11132215 · Cited by 1 · see citing works

Applications of generative and predictive AI in nutrition and dietetics: a narrative review

Bayram, H. M., & Öztürkcan, A. (2025) · Informatics for Health and Social Care · doi:10.1080/17538157.2025.2560834 · Cited by 8 · see citing works

POTENTIAL APPLICATIONTS OF ARTIFICIAL INTELLIGENCE IN NUTRITION SCIENCE

Martynchuk, O., Вацлавовна, Б.-П. Л., Shvets, O., & Altanova, A. (2025) · Human and nation s health · doi:10.31548/humanhealth.1.2025.100

Artificial Intelligence in Nutrition and Dietetics: A Comprehensive Review of Current Research

Panayotova, G. G. (2025) · Healthcare · doi:10.3390/healthcare13202579 · Cited by 63 · see citing works

Artificial Intelligence and Machine Learning for Personalized Nutrition and Health ManagementPreprint

Gupta, H. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6401958