МОЖЛИВОСТІ ЗАСТОСУВАННЯ ШТУЧНОГО ІНТЕЛЕКТУ В НУТРИЦІОЛОГІЇ

Автор(и)

  • Олександр Мартинчук Національний університет біоресурсів і природокористування України Автор https://orcid.org/0000-0002-8575-5589
  • Лариса Баль-Прилипко Національний університет біоресурсів і природокористування України Автор https://orcid.org/0000-0002-9489-8610
  • Олег Швець Національний університет біоресурсів і природокористування України Автор https://orcid.org/0000-0002-1434-4344
  • Альона Альтанова Національний університет біоресурсів і природокористування України Автор https://orcid.org/0000-0002-2783-4932

DOI:

https://doi.org/10.31548/humanhealth.1.2025.100

Ключові слова:

алгоритми машинного навчання, персоналізовані дієтичні рекомендації, харчова поведінка, оптимізація здоров'я, етичні стандарти

Анотація

Останнім часом відбувається хвилеподібне зростання інтересу до потенціалу штучного інтелекту (ШІ) практично у всіх галузях науки і техніки. Враховуючи цей тренд, слід зазначити, що наявні алгоритми машинного навчання перебувають на стадії постійного розвитку та вдосконалення. Вони є залежними від сателітних технологій, що дозволяють якісно збирати необхідну для подальшого аналізу інформацію. Цілком зрозуміло, що сучасні моделі ШІ володіють не тільки перевагами, а й недоліками, що динамічно змінюються. Можливості застосування ШІ потребують подальшого вивчення, модернізації та оцінки з точки зору практичної цінності. В нашій роботі ми сконцентрувались на поточних можливостях застосування ШІ в нутриціології, зокрема у контексті формування персоніфікованих дієтичних рекомендацій, оптимізації здоров’я та підтримки наукових досліджень. Розуміючи потенціал ШІ для вдосконалення роботи нутриціологів та покращення впливу на показники здоров’я людини ми спробували відокремити перспективні аспекти використання цієї технології від реального стану речей.

Метою дослідження став аналіз сучасних технологій і методів ШІ для оцінки їхнього впливу на розвиток нутриціології, окресленні переваг і обмежень технологій, а також перспектив подальшого використання.

Методи дослідження включали систематичний аналіз наукових джерел, огляд сучасних технологій і моделей ШІ, що активно використовуються для персоналізації харчування. Особлива увага приділялася можливостям комп’ютерного зору, алгоритмів глибокого машинного навчання та носимих пристроїв для аналізу поживної цінності їжі, моніторингу харчових звичок та адаптації дієтичних рекомендацій. Отримані результати свідчать, що ШІ здатен покращити якість збору та аналізу інформації про фактичне харчування людей, в тому числі шляхом нівелювання «людського чиннику». До переваг також слід віднести його здатність підвищити рівень персоналізації харчування та можливість виявляти тригери нездорової харчової поведінки, з подальшим формуванням рекомендацій щодо корекції. У взаємодії із носимими пристроями ця технологія дозволяє проводити динамічне коригування дієти. Тим не менш, технологія залишається залежною від прогресу у розвитку носимих пристроїв, потребує уніфікованих підходів та алгоритмів використання.

Практична цінність роботи полягає у формуванні критичного відношення до можливостей імплементації цієї технології у практичну та наукову діяльність фахівців у галузі нутриціології.

Посилання

Abhari, K.A., Williams, B., Hafeez, Z., & Kazemi, M. (2021). Artificial Intelligence Applications in Assessing Dietary Intake: A Scoping Review. Nutrients, 13, 4105. https://doi.org/10.3390/nu13124105

Bahirat, A. D., Dixit, B., & Dixit, A. (2024). Diet Consultation Using Artificial Intelligence. Food Science and Technology, 12(1), 24–47. https://doi.org/10.13189/fst.2024.120103

Aguilar, E., Nagarajan, B., Remeseiro, B., & Radeva, P. (2022). Bayesian deep learning for semantic segmentation of food images. Computers and Electrical Engineering, 103, 108380. https://doi.org/10.1016/j.compeleceng.2022.108380

Nutrition research priorities - american society for nutrition. (б. д.). American Society for Nutrition. https://nutrition.org/public-affairs/nutrition-research-agenda/

Amugongo, L. M., Kriebitz, A., Boch, A., & Lütge, C. (2022). Mobile computer vision-based applications for food recognition and volume and calorific estimation: A systematic review. Healthcare, 11(1), 59. https://doi.org/10.3390/healthcare11010059

Anderson, J., & Brown, R. (2022). Artificial Intelligence in Nutrition Science: A Comprehensive Review. Journal of Nutritional Health, 10(2), 123-145.

Theodore Armand, T. P., Nfor, K. A., Kim, J.-I., & Kim, H.-C. (2024). Applications of artificial intelligence, machine learning, and deep learning in nutrition: A systematic review. Nutrients, 16(7), 1073. https://doi.org/10.3390/nu16071073

Ates, H.C., Nguyen, P.Q., Gonzalez-Macia, L., Morales-Narváez, E., Güder, F., Collins, J.J., & Dincer, C. (2022). End-to-End Design of Wearable Sensors. Nature Reviews Materials, 7, 887–907. https://doi.org/10.1038/s41578-022-00460-x

Azzimani, K., Bihri, H., Dahmi, A., Azzouzi, S., & Charaf, M. E. H. (2022). An AI based approach for personalized nutrition and food menu planning. У 2022 IEEE 3rd international conference on electronics, control, optimization and computer science (ICECOCS). IEEE. https://doi.org/10.1109/icecocs55148.2022.9983099

Baker, R., & Jackson, T. (2019). Reducing Chronic Disease Risk Through AI-Powered Diets. Journal of Preventive Health, 14(1), 99-116.

Baowaly, M. K., Lin, C.-C., Liu, C.-L., & Chen, K.-T. (2018). Synthesizing electronic health records using improved generative adversarial networks. Journal of the American Medical Informatics Association, 26(3), 228–241. https://doi.org/10.1093/jamia/ocy142

Begum Kalyoncu Atasoy, Z., Avery, A., & Goktas, P. (2024). Artificial Intelligence-Powered Nutrition Strategies: A Focus on Vulnerable Populations. Kompass Nutrition & Dietetics, 23(1), 49-52. https://10.1159/000538139

Brown, M., & Taylor, D. (2023). Wearable Devices and IoT in Nutrition Monitoring. Journal of Connected Health, 19(1), 88-104.

Brown, A.W., Bohan Brown, M.M., & Allison, D.B. (2021). Using machine learning to personalize diet recommendations: A scoping review. Journal of the Academy of Nutrition and Dietetics, 121, 1995-2011. https://doi.org/10.1016/j.jand.2021.05.004

Busad, A., Suchitha, G., Darshan Gowda, T., Lakshmi, S., & Shalini Gowda, K. (2023). Computer Vision based Food Recognition with Nutrition Analysis. International Journal of Creative Research Thoughts (IJCRT), 11(1), 254-261. Retrieved at: https://www.ijcrt.org/papers/IJCRT2301042.pdf

Campbell, R., & Foster, J. (2021). AI and Computer Vision in Food Analysis. Journal of Food Safety and Quality, 20(1), 99-115.

Carter, N., & Miller, S. (2022). Integrating Genetic Data for Personalized Nutrition. Journal of Genomics and Nutrition, 9(3), 150-167.

Chen, H., & Wang, Q. (2018). Machine Learning in Nutrition: Past, Present and Future. Journal of Artificial Intelligence in Healthcare, 7(2), 101-119.

Chen, M., Dhingra, K., Wu, W., Yang, L., Sukthankar, R., & Yang, J. (2009). PFID: Pittsburgh fast-food image dataset. In International Conference on Image Processing (289-292). Cairo, Egypt. https://10.1109/ICIP.2009.5413511

Chew, HSJ, Chew, NW, Loong, SSE, Lim, SL, Tam, WSW, Chin, YH, Chao, AM, Dimitriadis, GK, Gao, Y, So, JBY, Shabbir, A, & Ngiam, KY. (2024). Effectiveness of an Artificial Intelligence-Assisted App for Improving Eating Behaviors: Mixed Methods Evaluation. Journal of Medical Internet Research, 7, 26:e46036. https://10.2196/46036.

Lozano, CP, Canty, EN, Saha, S, Broyles, ST, Beyl, RA, Apolzan, JW, Martin, CK. (2023). Validity of an Artificial Intelligence-Based Application to Identify Foods and Estimate Energy Intake Among Adults: A Pilot Study. Current Developments in Nutrition, 7(11), 102009. https://10.1016/j.cdnut.2023.102009.

Coates, A.M., Siervo, M., & Lindley, M.R. (2020). Artificial intelligence and the future of nutritional science. Proceedings of the Nutrition Society, 79, 425-430. https://doi.org/10.1017/S0029665120006733

Collins, H., & Richardson, S. (2017). Integrating Diverse Data for Personalized Nutrition. Journal of Integrative Health, 6(4), 275-292.

Côté, M., & Lamarche, B. (2021). Artificial intelligence in nutrition research: Perspectives on current and future applications. Applied Physiology, Nutrition, and Metabolism, 15, 1-8. https://10.1139/apnm-2021-0448

Cunha, C., Duarte, P., & Oliveira, R. (2023). Nutrition Control System Based on Short-term Personal Demands. Procedia Computer Science, 224, 565–571. https://doi.org/10.1016/j.procs.2023.09.082

Davis, K., & Robinson, F. (2017). Deep Learning for Chronic Disease Management. Journal of Healthcare Technology, 8(2), 123-138.

Detopoulou, P. et al. (2024). Artificial intelligence, nutrition, and ethical issues: A mini-review. Clinical Nutrition Open Science, 50, 46-56.

Gibson, R. S. (2005). Principles of Nutritional Assessment (2nd ed.). Oxford University Press.

Farinella, G. M., Furnari, A., & Ragusa, F. (2022). Sistemi di Visione Artificiale Indossabili in Ambienti Industriali. In Ital-IA Convegno Nazionale CINI sull'Intelligenza Artificiale Roma.

Gopalan, H.S., & Jayaraman, R. (2020). A review of the applications of artificial intelligence in dietetics. Journal of the American Dietetic Association, 120, 962-972. https://doi.org/10.1016/j.jada.2020.02.014

Hall, K., & Martin, G. (2021). The Future of AI in Nutritional Science. Journal of Advanced Nutritional Studies, 16(3), 199-216.

Hasan, W. U., K. Tuz Zaman, M. S. A. T. Zadeh and J. Li (2022). Eat This, Not That! – a Personalised Restaurant Menu Decoder That Helps You Pick the Right Food. In International Conference on E-health Networking, Application & Services (HealthCom) (pp. 43-48). Genoa, Italy. https://10.1109/HealthCom54947.2022.9982770

Hiraguchi, H, Perone, P, Toet, A, Camps, G, & Brouwer, A-M. (2023). Technology to Automatically Record Eating Behavior in Real Life: A Systematic Review. Sensors, 23(18), 7757. https://doi.org/10.3390/s23187757

Hoang, YN, Chen, Y, Ho, DKN, et al. (2023). Consistency and Accuracy of Artificial Intelligence for Providing Nutritional Information. JAMA Network Open, 6(12):e2350367. https://10.1001/jamanetworkopen.2023.50367

Islam, T., Joyita, A. R., Alam, M. G. R., Mehedi Hassan, M., Hassan, M. R. and Gravina, R., (2023). Human-Behavior-Based Personalized Meal Recommendation and Menu Planning Social System. IEEE Transactions on Computational Social Systems, vol. 10, no. 4, 2099-2110. https://10.1109/TCSS.2022.3213506

Johnson, A., & White, P. (2019). Historical Perspectives on the Use of AI in Nutrition. Journal of Nutrition and Food Technology, 14(1), 45-59.

Johnson, R.K., Foster, G.D., Gallagher, K.I., & Waite, R.R. (2021). The potential of AI-driven personalized nutrition for addressing public health challenges: A review. Nutrition Reviews, 79, 410-425. https://doi.org/10.1093/nutrit/nuaa077

Jones, J.L., Wilson, M.L., Williams, A.C., & Williams, M.T. (2021). Integrating AI into dietary interventions: Opportunities and challenges. Journal of Nutritional Science, 10, e31. https://doi.org/10.1017/jns.2021.26

Joshi, S., Bisht, B., Kumar, V. et al. (2024). Artificial intelligence assisted food science and nutrition perspective for smart nutrition research and healthcare. Systems Microbiology and Biomanufacturing, 4, 86–101. https://doi.org/10.1007/s43393-023-00200-4

Vandeputte, J, Herold, P, Kuslii, M, Viappiani, P, Muller, L, Martin, C, Davidenko, O, Delaere, F, Manfredotti, C, Cornuéjols, A, & Darcel, N. (2023). Principles and Validations of an Artificial Intelligence-Based Recommender System Suggesting Acceptable Food Changes. The Journal of Nutrition, 153(2), 598-604. https:// 10.1016/j.tjnut.2022.12.022

Kansaksiri, P., Panomkhet, P., & Tantisuwichwong, N. (2023). Smart Cuisine: Generative Recipe & ChatGPT Powered Nutrition Assistancefor Sustainable Cooking. Procedia Computer Science, 225, 2028–2036.

Kaushal, S., Tammineni, D. K., Rana, P., Sharma, M., Sridhar, K., & Chen, H.-H. (2024). Computer vision and deep learning-based approaches for detection of food nutrients/nutrition: New insights and advances. Trends in Food Science & Technology, 146, 104408. https://doi.org/10.1016/j.tifs.2024.104408

Kavita, M. S. (2021). Application of Artificial Intelligence on Nutrition Assessment and Management. European Journal of Pharmaceutical and Medical Research, 8, 170-174.

Kazanskiy, N. L., Khonina, S. N., & Butt, M. A. (2023). Smart Contact Lenses-A Step towards Non-Invasive Continuous Eye Health Monitoring. Biosensors, 13(10), 933. https://doi.org/10.3390/bios13100933

Kelly, J.T., Collins, P.F., McCamley, J., Ball, L., Roberts, S., & Campbell, K.L. (2021). Digital disruption of dietetics: Are we ready? Journal of Human Nutrition and Dietetics, 34, 134–146.

Khamis, M.M., Aglave, R., & Khamis, A. (2021). AI-driven solutions in dietetics: Emerging trends and applications. Nutrition and Metabolic Insights, 14, 117863882110094. https://doi.org/10.1177/11786388211009466

Khan, M. I., Acharya, B., & Chaurasiya, R. K. (2022). iHearken: Chewing sound signal analysis based food intake recognition system using Bi-LSTM softmax network. Computer Methods and Programs in Biomedicine, 221, 106843. https://doi.org/10.1016/j.cmpb.2022.106843

Khan, M.I., Acharya, B., & Chaurasiya, R.K. (2022). iHearken: Chewing Sound Signal Analysis Based Food Intake Recognition SystemUsing Bi-LSTM Softmax Network. Computer Methods and Programs in Biomedicine, 221, 106843

Khan, M, & Agarwal, S, & Vatsa, M, & Singh, R, & Singh, K. (2023). NutriAI: AI-Powered Child Malnutrition Assessment in Low-Resource Environments. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (IJCAI-23), Special Track on AI for Good (Projects) (рр. 6378- 6385). https://doi.org/10.24963/ijcai.2023/708.

Kim, S., & Park, J. (2022). Virtual and Augmented Reality for Nutrition Education. Journal of Interactive Health, 14(2), 156-173.

Kirk, D., Kok, E., Tufano, M., Tekinerdogan, B., Feskens, E.J.M., & Camps, G. (2022). Machine Learning in Nutrition Research. Advances in Nutrition, 13, 2573–2589.

Lee, A, Mavaddat, N, Wilcox, AN, et al. (2019). BOADICEA: a comprehensive breast cancer risk prediction model incorporating genetic and nongenetic risk factors. Genetics in Medicine, 21(8), 1708-1718. https://10.1038/s41436-018-0406-9

Lewis, T., & Lee, K. (2023). Personalized Diets Using AI: Benefits and Challenges. Journal of Health Informatics, 18(1), 56-72.

Li, T, Wei, W, Xing, S, Min, W, Zhang, C, & Jiang, S. (2023). Deep Learning-Based Near-Infrared Hyperspectral Imaging for Food Nutrition Estimation. Foods, 12(17), 3145. https://doi.org/10.3390/foods12173145

Li, D., Zaki, M.J., & Chen, C. (2023). Health-Guided Recipe Recommendation over Knowledge Graphs. Journal of Web Semantics, 75, 100743.

Liang, Y., & Li, J. (2017). Computer vision-based food calorie estimation: dataset, method, and experiment. arXiv:1705.07632

Liu, S., Liu, Y., & Han, S. (2016). Calorie Mama: A Mobile-based Food Recognition System for Dietary Tracking.

Lo, Frank P-W., Qiu, J., Wang, Z., Chen, J., Xiao, B., Yuan, W., ... & Lo, B. (2023). Dietary Assessment with Multimodal ChatGPT: A Systematic Analysis. arXiv preprint arXiv:2312.08592.

Lu, Y, Stathopoulou, T, Vasiloglou, MF, et al. (2019). An Artificial Intelligence-Based System for Nutrient Intake Assessment of Hospitalised Patients. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), (рр. 5696-5699). https://10.1109/EMBC.2019.8856889

Lu, Y, Stathopoulou, T, Vasiloglou, MF, Christodoulidis, S, Blum, B, Walser, T, Meier, V, Stanga, Z, Mougiakakou, SG. (2019). An Artificial Intelligence-Based System for Nutrient Intake Assessment of Hospitalised Patients. In 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), (pp. 5696-5699). Berlin, Germany. https://10.1109/EMBC.2019.8856889

Lucassen, D.A., Lasschuijt, M.P., Camps, G., Van Loo, E.J., Fischer, A.R.H., de Vries, R.A.J., Haarman, J.A.M., Simons, M., de Vet, E., Bos-de Vos, M., et al. (2021). 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, 7877. https://doi.org/10.3390/ijerph18157877

Ma, L., Chen, H., Wu, Y., Yuan, H., & Xu, F. (2021). The Role of Artificial Intelligence in Nutrition Research: A Review of Current Applications and Future Directions. Frontiers in Nutrition, 8, 663228. https://doi.org/10.3389/fnut.2021.663228

Martin, CK, Kaya, S, & Gunturk, BK. (2009). Quantification of food intake using food image analysis. In Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2009:6869-72. https://10.1109/IEMBS.2009.5333123

Martinez, A., & Clark, D. (2016). AI in Personalized Treatment Plans. Journal of Medical Informatics, 10(3), 245-262.

Martínez-Rodríguez, A, Martínez-Olcina, M, Mora, J, Navarro, P, Caturla, N, & Jones, J. (2022). New App-Based Dietary and Lifestyle Intervention on Weight Loss and Cardiovascular Health. Sensors, 22(3):768. https://doi.org/10.3390/s22030768

Matheny, ME, Whicher, D, Thadaney Israni, S. (2020). Artificial Intelligence in Health Care: A Report From the National Academy of Medicine. Journal of the American Medical Association, 323(6):509-510. https://10.1001/jama.2019.21579

Mayo, RC, & Leung, J. (2018). Artificial intelligence and deep learning-Radiology's next frontier? Clinical Imaging, 49, 87-88. https://10.1016/j.clinimag.2017.11.007

Mc Ginnis, JM, Williams-Russo, P, & Knickman, JR. (2002). The case for more active policy attention to health promotion. Health affairs (Millwood), 21(2), 78-93. https://10.1377/hlthaff.21.2.78

Mertes, G., Ding, L., Chen, W., Hallez, H., Jia, J., & Vanrumste, B. (2020). Measuring and localizing individual bites using a sensor augmented plate during unrestricted eating for the aging population. IEEE Journal of Biomedical and Health Informatics, 24, 1509–1518.

Haman, M, Školník, M, Lošťák, & M. (2024). AI dietician: Unveiling the accuracy of ChatGPT's nutritional estimations. Nutrition, 119, 112325. https://doi.org/10.1016/j.nut.2023.112325

Migliorelli, C., Ros-Freixedes, L., Gomez-Martinez, M., Sistach-Bosch, L., & Orte, S. (2023). Carpediem: Investigating the Interactions of Health Pillars to Design Holistic Recommendations for Achieving Long-Term Changes in Lifestyle Behaviours. In: Arai, K. (eds) Intelligent Computing. SAI 2023. Lecture Notes in Networks and Systems, vol 711. Springer, Cham. https://doi.org/10.1007/978-3-031-37717-4_91

Miyazawa, T. et al. (2022). Artificial intelligence in food science and nutrition: a narrative review. Nutrition Reviews, 80(12), 2288-2300. doi:10.1093/nutrit/nuacd133

Moore, D., & Adams, L. (2021). Real-Time Dietary Recommendations Using AI. Journal of Real-Time Health Monitoring, 12(4), 89-106.

Naja, F., Taktouk, M., Matbouli, D. et al. (2024). Artificial intelligence chatbots for the nutrition management of diabetes and the metabolic syndrome. Eur J Clinical Nutrition, 78, 887–896. https://doi.org/10.1038/s41430-024-01476-y

Neha, K., P. Sanjan, S. Hariharan, S. Namitha, A. Jyoshna and A. B. Prasad (2023). Food Prediction based on Recipe using Machine Learning Algorithms. In Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS), pp. 411-416. Trichy, India, https://10.1109/ICAISS58487.2023.10250758

Nutritional Cancer Institute (2024). URL: https://www.cancer.gov/publications/dictionaries/cancer-terms/def/nutritionist

Olivera, D., Sampaio, C., Lima, T., & Ramos, L. (2021). Machine Learning in Nutrition Research: Algorithms and Applications. Nutrition Research Reviews, 34, 295-306. URL: https://doi.org/10.1017/S0954422412000174

Owoc, M. L., Sawicka, A., & Weichbroth, P. (2021). Artificial Intelligence Technologies in Education: Benefits, Challenges and Strategies of Implementation. In M. L. Owoc, & M. Pondel (Eds.), Artificial Intelligence for Knowledge Management (pp. 37-58). Springer. https://doi.org/10.1007/978-3-030-85001-2_4

Papapanagiotou, V., Ganotakis, S., & Delopoulos, A. (2021). Bite-weight estimation using commercial ear buds. In Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (pp. 7182–7185). Guadalajara, Jalisco, Mexico.

Parikh, RB, Teeple, S, & Navathe, AS. (2019). Addressing Bias in Artificial Intelligence in Health Care. Journal of the American Medical Association, 322(24), 2377-2378. https://10.1001/jama.2019.18058

Parker, J., & Nelson, E. (2023). Comprehensive Models for Disease Prediction Using AI. Journal of Computational Medicine, 17(1), 67-82.

Patel, S., & Kumar, R. (2017). Deep Learning for Personalized Nutrition: A Review. Journal of Computational Health, 5(3), 211-230.

Qiu, J., F. P., Lo, W. and Lo, B. (2019). Assessing Individual Dietary Intake in Food Sharing Scenarios with a 360 Camera and Deep Learning. In IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN) (pp. 1-4). Chicago, IL, USA. https://10.1109/BSN.2019.8771095

Rahmath Nisha, S., Aarthi, M., Shyamala, C., Pavithra, M., Vallipriyadharshini, K., & Raj, B.S. (2023). Intelligent Nutrition AssistantApplication. In Proceedings of the 2023 12th International Conference on Advanced Computing (ICoAC) (pp. 1–6). Chennai, India.

Ribeiro, D, Barbosa, T, Ribeiro, J, Sousa, F, Vieira, EF, Silva, M, & Silva, A. (2022). SousChef System for Personalized Meal Recommendations: A Validation Study. Applied Sciences, 12(2), 702. https://doi.org/10.3390/app12020702

Roberts, M., & Green, J. (2016). Early Applications of AI in Nutritional Analysis. Journal of Food and Nutrition Research, 11(2), 85-102.

Rokhva, S., Teimourpour, B., & Soltani, A. H. (2024). Computer Vision in the Food Industry: Accurate, Real-time, and Automatic Food Recognition with Pretrained MobileNetV2. arXiv preprint arXiv:2405.11621.

Safitri, S., Mantoro, T., Bhakti, M.A.C., & Wandy, W. (2023). Cooking and Food Information Chatbot System Using GPT-3. In Proceedingsof the 2023 IEEE 9th International Conference on Computing, Engineering and Design (ICCED) (pp. 1–6). Kuala Lumpur, Malaysia.

Sak, J, & Suchodolska, M. (2021). Artificial Intelligence in Nutrients Science Research: A Review. Nutrients, 13(2), 322. https://doi.org/10.3390/nu13020322

Salinari, A., Machì, M., Armas Diaz, Y., Cianciosi, D., Qi, Z., Yang, B., Ferreiro Cotorruelo, M.S., Villar, S.G., Dzul Lopez, L.A., Battino, M., et al. (2023). The Application of Digital Technologies and Artificial Intelligence in Healthcare: An Overview on Nutrition Assessment. Diseases, 11, 97.

Sanders, P., & Ross, M. (2020). Spectroscopy and AI in Food Composition Analysis. Journal of Food Composition and Analysis, 19(3), 211-229.

Saxena, R, Saxena, RR, & Saxena, AR. (2018). Microbiomics in the Molecular Era: A Bird’s Eye View into the Future of Personalized Medicine. Acta Scientific Microbiology, 1(8), 34-39.

Saxena, R., Sharma, V., Saxena, A. R., & Patel, A. (2024). Harnessing AI and Gut Microbiome Research for Precision Health. Journal of Artificial Intelligence General Science (JAIGS), 74–88. https://doi.org/10.60087/jaigs.v3i1.68

Schuller, D.M., & Schuller, B.W. (2020). The Challenge of Automatic Eating Behaviour Analysis and Tracking. In Costin, H., Schuller, B., Florea, A. (eds) Recent Advances in Intelligent Assistive Technologies: Paradigms and Applications. Intelligent Systems Reference Library. (Vol 170). Springer, Cham. https://doi.org/10.1007/978-3-030-30817-9_8

Sen, R., Karthikeyan, K., Prabhakar, P., Vishwakarma, J., Gupta, G., Mishra, S., Mishra, A., Chaurasia, J., Hashmi, S. A. R., Mondal, D., Solanki, P, Srivastava, A., Dhand, C., & Dwivedi, N. (2022). Fast Tracking of Adulterants and Bacterial Contamination in Food via Raman and Infrared Spectroscopies: Paving the Way for a Healthy and Safe World. Sensors & Diagnostics. 1. https://10.1039/D1SD00046B

Shajari, S, Kuruvinashetti, K, Komeili, A, Sundararaj, U. (2023).The Emergence of AI-Based Wearable Sensors for Digital Health Technology: A Review. Sensors, 23(23), 9498. https://doi.org/10.3390/s23239498

Sharma, V., Sharma, V., Khan, A., Wassmer, DJ., Schoenholtz, M.D., Hontecillas, R., Bassaganya-Riera, J., Z. and R. and Abedi, V. (2020). Malnutrition, Health and the Role of Machine Learning in Clinical Setting. Frontiers in Nutrition, 7, 44. https://10.3389/fnut.2020.00044

Sharma, S. K., & Gaur, S. (2024). Optimizing Nutritional Outcomes: The Role of AI in Personalized Diet Planning. International Journal for Research Publication and Seminar, 15(2), 107–116. https://doi.org/10.36676/jrps.v15.i2.15

Shima, H, Masuda, S, Date, Y, et al. (2017). Exploring the Impact of Food on the Gut Ecosystem Based on the Combination of Machine Learning and Network Visualization. Nutrients, 9(12), 1307. doi:10.3390/nu9121307

Singar, S, Nagpa, R, Arjmandi, BH, & Akhavan, NS. (2024). Personalized Nutrition: Tailoring Dietary Recommendations through Genetic Insights. Nutrients, 16(16), 2673. https://doi.org/10.3390/nu16162673

Singh, S., Mishra, S., Rani, R., & Gupta, P.K. (2021). Role of AI in personalized nutrition and precision medicine: Current insights and future directions. Frontiers in Nutrition, 8, 748589. https://doi.org/10.3389/fnut.2021.748589

Smith, L., & Thompson, M. (2021). The Role of AI in Personalized Nutrition. Journal of Medical Nutrition, 15(3), 223-245.

Sonkusale, S. (2022). Smart Threads for Tissue-Embedded Bioelectronics. In Proceedings of the 2022 IEEE Custom Integrated Circuits Conference (CICC) (pp. 1–7). Newport Beach, CA, USA.

Sosa-Holwerda, A, Park, O-H, Albracht-Schulte, K, Niraula, S, Thompson, L, & Oldewage-Theron, W. (2024). The Role of Artificial Intelligence in Nutrition Research: A Scoping Review. Nutrients, 16(13), 2066. https://doi.org/10.3390/nu16132066

Stewart, L., Hughes, G. (2022). AI for Real-Time Health Monitoring and Recommendations. Journal of Mobile Health, 13(2), 112-130.

Miyazawa, T, Hiratsuka, Y, Toda, M, Hatakeyama, N, Ozawa, H, Abe, C, Cheng, TY, Matsushima, Y, Miyawaki, Y, Ashida, K, Iimura, J, Tsuda, T, Bushita, H, Tomonobu, K, Ohta, S, Chung, H, Omae, Y, Yamamoto, T, Morinaga, M, Ochi, H, Nakada, H, Otsuka, K, & Miyazawa, T. (2022). Artificial intelligence in food science and nutrition: a narrative review. Nutrition Reviews, Volume 80, Issue 12, 2288–2300. https://doi.org/10.1093/nutrit/nuac033

Takahashi, C., Matsushita, M., & Yamanishi, R. (2023). Exploration Cycle Finding a Better Dining Experience: A Framework of Meal-Plates. Procedia Computer Science, 225, 2902–2911.

Tan, P. Y., Moore, J. B., Bai, L., Tang, G., & Gong, Y. Y. (2022). In the context of the triple burden of malnutrition: A systematic review of gene-diet interactions and nutritional status. Critical Reviews in Food Science and Nutrition, 64(11), 3235–3263. https://doi.org/10.1080/10408398.2022.2131727

Theodore Armand, TP, Nfor KA, Kim, J-I, & Kim, H-C. (2024). Applications of Artificial Intelligence, Machine Learning, and Deep Learning in Nutrition: A Systematic Review. Nutrients, 16(7):1073. https://doi.org/10.3390/nu16071073

Thomas, D.M., Kleinberg, S., Brown, A.W., Crow, M., Bastian, N.D., Reisweber, N., Lasater, R., Kendall, T., Shafto, P., Blaine, R., et al. (2022). Machine learning modeling practices to support the principles of AI and ethics in nutrition research. Nutrition & Diabetes, 12, 48.

Topol, EJ. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44-56. https://10.1038/s41591-018-0300-7 66

Hashimoto, DA, Rosman, G, Rus, D, Meireles, OR. (2018). Artificial Intelligence in Surgery: Promises and Perils. Annals of Surgery, 268(1):70-76. https://10.1097/SLA.0000000000002693

Tsolakidis, D, Gymnopoulos, LP, & Dimitropoulos, K. (2024). Artificial Intelligence and Machine Learning Technologies for Personalized Nutrition: A Review. Informatics, 11, 62. https://10.3390/informatics11030062

Turner, J., & Morgan, L. (2018). Automation in Food Processing with AI. Journal of Food Technology, 15(3), 183-198.

Kakani, V, Nguyen, VH, Kumar, BP, Kim, H, & Pasupuleti, VR. (2020). A critical review on computer vision and artificial intelligence in food industry. Journal of Agriculture and Food Research, 2, 100033. https://doi.org/10.1016/j.jafr.2020.100033.

Vitolins, M.Z., & Case, T.L. (2020). What Makes Nutrition Research So Difficult to Conduct and Interpret? Diabetes Spectrum, 33, 113–117.

Wahl, B., Cossy-Gantner, A., Germann, S., & Schwalbe, N.R. (2018). Artificial intelligence (AI) and global health: How can AI contribute to health in resource-poor settings? BMJ Global Health, 3, e000798.

Wang, F, & Preininger, A. (2019). AI in Health: State of the Art, Challenges, and Future Directions. Yearbook of Medical Informatics, 28(1), 16-26. https://10.1055/s-0039-1677908

Wang, L., Allman-Farinelli, M., Yang, J.-A., Taylor, J.C., Gemming, L., Hekler, E., & Rangan, A. (2022). Enhancing Nutrition Care Through Real-Time, Sensor-Based Capture of Eating Occasions: A Scoping Review. Frontiers in Nutrition, 9. https://doi.org/10.3389/fnut.2022.852984

Wang, M., Yang, Y., Min, J., Song, Y., Tu, J., Mukasa, D., Ye, C., Xu, C., Heflin, N., McCune, J.S., et al. (2022). A Wearable ElectrochemicalBiosensor for the Monitoring of Metabolites and Nutrients. Nature Biomedical Engineering, 6, 1225–1235.

Wang, S., Xia, K., Yang, Y., Qiu, R., Qi, Y., Miao, Q., Xie, W., & Liu, T. A. (2022). Recommender System for Healthy Food Choices Based on Integer Programming. In Advances in Transdisciplinary Engineering; Chen, C.-H., Scapellato, A., Barbiero, A., Korzun, D.G., Eds.; IOS Press: Amsterdam, The Netherlands.

Watkins, B., Odallo, L., & Yu, J. (2024). Artificial intelligence for the practical assessment of nutritional status in emergencies. Expert Systems, 41, e13550.

West, D.J., Turner, A., Hughes, G., & Jeukendrup, A.E. (2020). Personalized nutrition using artificial intelligence: A review. Nutrients. 12, 456. https://doi.org/10.3390/nu12020456

Wilson-Barnes, S, Pongcharoenyong, L, Gymopoulos, L, et al. (2022). The utility of breath volatile organic compound (VOC) sampling as a biomarker of sub-optimal nutritional status: a UK pilot study. Proceedings of the Nutrition Society. 81(OCE4):E90. https://10.1017/S0029665122001197

Wright, B., & Allen, C. (2019). Portable Devices for Food Safety Using AI. Journal of Food Safety Technology, 18(2), 145-162.

Wu, H. et al. (2022). A Visually-Aware Food Analysis System for Diet Management. In IEEE International Conference on Multimedia and Expo Workshops (ICMEW) (pp. 1-1). Taipei City, Taiwan. https://10.1109/ICMEW56448.2022.9859471

Xia, Y., Khamis, M., Fernandez, F. A., Heidari, H., Butt, H., Ahmed, Z., Wilkinson, T., & Ghannam, R. (2022). State-of-the-Art in Smart Contact Lenses for Human-Machine Interaction. IEEE Transactions on Human-Machine Systems. URL: https://discovery.ucl.ac.uk/id/eprint/10180151/1/State-of-the-Art%20in%20Smart%20Contact%20Lenses%20for%20Human-Machine%20Interaction.pdf

Yang, J. (2022). Genetic Data Analysis and Business Process Management Platform for Personalized Nutrition Service. In: Gervasi, O., Murgante, B., Misra, S., Rocha, A.M.A.C., Garau, C. (eds) Computational Science and Its Applications – ICCSA 2022 Workshops. Lecture Notes in Computer Science. vol 13381. Springer, Cham. https://doi.org/10.1007/978-3-031-10548-7_40

Yang, J. (2023). Using nutrigenomics to guide personalized nutrition supplementation for bolstering immune system. Health Information Science and Systems, 11, 4. https://doi.org/10.1007/s13755-022-00208-5

Yang, S., Chen, M., Pomerleau, D., and Sukthankar, R. (2010). Food recognition using statistics of pairwise local features. Computer Vision and Pattern Recognition (CVPR). URL: https://ieeexplore.ieee.org/document/5 539907

Yera, R., Alzahrani, A.A., & Martínez, L. (2022). Exploring Post-Hoc Agnostic Models for Explainable Cooking Recipe Recommendations. Knowledge-Based Systems, 251, 109216.

Yoon, J, Drumright, LN, & van der Schaar, M. (2020). Anonymization Through Data Synthesis Using Generative Adversarial Networks (ADS-GAN). IEEE Journal of Biomedical and Health Informatics, 24(8), 2378-2388. https://10.1109/JBHI.2020.2980262

Young, E., & Harris, B. (2018). AI in Disease Prediction: Applications in Nutrition. Journal of Predictive Medicine, 6(3), 200-219.

Eskin, Yu, and Mihailidis, A. (2012). An Intelligent Nutritional Assessment System. AAAI Technical report FS-12-01. Association for the Advancement of Artificial Intelligence, pp 2-6.

Zamanillo-Campos, R, Fiol-deRoque, MA, Serrano-Ripoll, MJ, Mira-Martínez, S, & Ricci-Cabello, I. (2023). Development and evaluation of DiabeText, a personalized mHealth intervention to support medication adherence and lifestyle change behaviour in patients with type 2 diabetes in Spain: A mixed-methods phase II pragmatic randomized controlled clinical trial. International Journal of Medical Informatics, 176, 105103. https://10.1016/j.ijmedinf.2023.105103

Zhou, T, Wang, M, Ma, H, Li, X, Heianza, Y, & Qi, L. (2021). Dietary Fiber, Genetic Variations of Gut Microbiota-derived Short-chain Fatty Acids, and Bone Health in UK Biobank. Journal of Clinical Endocrinology & Metabolism, 106(1), 201-210. https://10.1210/clinem/dgaa740

Zuhair, V., Babar, A., Ali, R., Oduoye, M.O., Noor, Z., Chris, K., Okon, I.I., & Rehman, L.U. (2024). Exploring the Impact of Artificial Intelligence on Global Health and Enhancing Healthcare in Developing Nations. Journal of Primary Care & Community Health, 15, 21501319241245847.

Zukowska, J., Matuszewski, T., Gacek, M., & Sadowska, D. (2020). The use of artificial intelligence in dietetics-current state and potential applications. Nutrients, 3619. https://doi.org/10.3390/nu12123619

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2025-04-02

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Мартинчук, О., Баль-Прилипко , Л., Швець, О., & Альтанова, А. (2025). МОЖЛИВОСТІ ЗАСТОСУВАННЯ ШТУЧНОГО ІНТЕЛЕКТУ В НУТРИЦІОЛОГІЇ. Здоров’я людини і нації, 3(1), 100-125. https://doi.org/10.31548/humanhealth.1.2025.100

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