The Role of Artificial Intelligence in Nutrition Research: A Scoping Review
Open accessAbstract
Artificial intelligence (AI) refers to computer systems doing tasks that usually need human intelligence. AI is constantly changing and is revolutionizing the healthcare field, including nutrition. This review’s purpose is four-fold: (i) to investigate AI’s role in nutrition research; (ii) to identify areas in nutrition using AI; (iii) to understand AI’s future potential impact; (iv) to investigate possible concerns about AI’s use in nutrition research. Eight databases were searched: PubMed, Web of Science, EBSCO, Agricola, Scopus, IEEE Explore, Google Scholar and Cochrane. A total of 1737 articles were retrieved, of which 22 were included in the review. Article screening phases included duplicates elimination, title-abstract selection, full-text review, and quality assessment. The key findings indicated AI’s role in nutrition is at a developmental stage, focusing mainly on dietary assessment and less on malnutrition prediction, lifestyle interventions, and diet-related diseases comprehension. Clinical research is needed to determine AI’s intervention efficacy. The ethics of AI use, a main concern, remains unresolved and needs to be considered for collateral damage prevention to certain populations. The studies’ heterogeneity in this review limited the focus on specific nutritional areas. Future research should prioritize specialized reviews in nutrition and dieting for a deeper understanding of AI’s potential in human nutrition.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
AI-powered platforms, wearable devices, and continuous glucose monitors enhance adherence by tracking biometrics and offering actionable insights to users, making weight management dynamic and adaptive to individual needs.
“These tools enhance adherence by tracking biometrics and offering actionable insights to users, making weight management dynamic and adaptive to individual needs [96,97,98,99,100,101,102].”
From Beyond Calories: Individual Metabolic and Hormonal Adaptations Driving Variability in Weight Management—A State-of-the-Art Narrative Review (2024) · cites this paper as [101]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
1 retracted
Chatelan, A., Clerc, A., & Fonta, P.-A. (2023) · Journal of the Academy of Nutrition and Dietetics · doi:10.1016/j.jand.2023.08.001 · Cited by 100 · see citing works
RetractedSystematic Literature Reviews in Software Engineering—A Systematic Literature Review
Kitchenham, B., Pearl Brereton, O., Budgen, D., Turner, M., Bailey, J., & Linkman, S. (2009) · Information and Software Technology · doi:10.1016/j.infsof.2008.09.009 · Cited by 4,537 · see citing works
Haenlein, M., & Kaplan, A. (2019) · California Management Review · doi:10.1177/0008125619864925 · Cited by 2,655 · see citing works
Wahl, B., Cossy-Gantner, A., Germann, S., & Schwalbe, N. R. (2018) · BMJ Global Health · doi:10.1136/bmjgh-2018-000798 · Cited by 796 · see citing works
On Defining Artificial Intelligence
Wang, P. (2019) · Journal of Artificial General Intelligence · doi:10.2478/jagi-2019-0002 · Cited by 740 · see citing works
Artificial intelligence: a survey on evolution, models, applications and future trends
Lu, Y. (2019) · Journal of Management Analytics · doi:10.1080/23270012.2019.1570365 · Cited by 641 · see citing works
Human- versus Artificial Intelligence
Korteling, J. E. (., van de Boer-Visschedijk, G. C., Blankendaal, R. A. M., Boonekamp, R. C., & Eikelboom, A. R. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.622364 · Cited by 619 · cited 2× · see citing works
“However, the distinctions and similarities between humans and AI are being widely debated [20].”
BackgroundReal-world data: A brief review of the methods, applications, challenges and opportunities
Liu, F., & Panagiotakos, D. (2022) · BMC Medical Research Methodology · doi:10.1186/s12874-022-01768-6 · Cited by 496 · see citing works
Oh, Y. J., Zhang, J., Fang, M.-L., & Fukuoka, Y. (2021) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-021-01224-6 · Cited by 278 · see citing works
Brief History of Artificial Intelligence
Muthukrishnan, N., Maleki, F., Ovens, K. L., Reinhold, C., Forghani, B., & Forghani, R. (2020) · Neuroimaging Clinics of North America · doi:10.1016/j.nic.2020.07.004 · Cited by 252 · see citing works
The role of artificial intelligence in medical imaging research
Tang, X. (2019) · BJR|Open · doi:10.1259/bjro.20190031 · Cited by 219 · see citing works
Babel, A., Taneja, R., Mondello Malvestiti, F., Monaco, A., & Donde, S. (2021) · Frontiers in Digital Health · doi:10.3389/fdgth.2021.669869 · Cited by 199 · cited 2× · see citing works
“…from Medicare revealed significant rates of non-adherence among patients; 76% of them were not taking their corresponding medicine, but when an AI-app was used providing daily reminders and instructions regarding the drug dosage to patients who had had a stroke, 100% adherence was achieved [62].”
BackgroundZuhair, V., Babar, A., Ali, R., Oduoye, M. O., Noor, Z., Chris, K., Okon, I. I., & Rehman, L. U. (2024) · Journal of Primary Care & Community Health · doi:10.1177/21501319241245847 · Cited by 197 · see citing works
Machine Learning in Nutrition Research
Kirk, D., Kok, E. J., Tufano, M., Teki̇nerdoğan, B., Feskens, E. J. M., & Camps, G. (2022) · Advances in Nutrition · doi:10.1093/advances/nmac103 · Cited by 194 · see citing works
Gibson, A., & Sainsbury, A. (2017) · Behavioral Sciences · doi:10.3390/bs7030044 · Cited by 181 · see citing works
Maher, C. A., Davis, C. R., Curtis, R. G., Short, C. E., & Murphy, K. J. (2020) · JMIR mHealth and uHealth · doi:10.2196/17558 · Cited by 165 · see citing works
Artificial intelligence, human intelligence and hybrid intelligence based on mutual augmentation
Jarrahi, M. H., Lutz, C., & Newlands, G. (2022) · Big Data & Society · doi:10.1177/20539517221142824 · Cited by 156 · see citing works
The credibility of dietary advice formulated by ChatGPT: Robo-diets for people with food allergies
Niszczota, P., & Rybicka, I. (2023) · Nutrition · doi:10.1016/j.nut.2023.112076 · Cited by 141 · see citing works
Artificial Intelligence in Nutrients Science Research: A Review
Sak, J. J., & Suchodolska, M. (2021) · Nutrients · doi:10.3390/nu13020322 · Cited by 139 · see citing works
The Age of Artificial Intelligence: Use of Digital Technology in Clinical Nutrition
Limketkai, B. N., Mauldin, K., Manitius, N., Jalilian, L., & Salonen, B. R. (2021) · Current Surgery Reports · doi:10.1007/s40137-021-00297-3 · Cited by 128 · see citing works
Exploring the Potential of Chat GPT in Personalized Obesity Treatment
Arslan, S. (2023) · Annals of Biomedical Engineering · doi:10.1007/s10439-023-03227-9 · Cited by 126 · see citing works
Morley, J., Murphy, L., Mishra, A., Joshi, I., & Karpathakis, K. (2021) · JMIR Formative Research · doi:10.2196/31623 · Cited by 119 · see citing works
Tahir, G. A., & Loo, C. K. (2021) · Healthcare · doi:10.3390/healthcare9121676 · Cited by 118 · see citing works
Artificial Intelligence in Nursing Education: Opportunities and Challenges
Glauberman, G., Ito-Fujita, A., Katz, S., & Callahan, J. (2023) · Hawai‘i Journal of Health & Social Welfare · doi:10.62547/yeis2105 · Cited by 114 · see citing works
Sun, H., Zhang, K., Lan, W., Gu, Q., Jiang, G., Yang, X., Qin, W., & Han, D. (2023) · Journal of Medical Internet Research · doi:10.2196/51300 · Cited by 105 · see citing works
A Review of Image-Based Food Recognition and Volume Estimation Artificial Intelligence Systems
Konstantakopoulos, F. S., Georga, E. I., & Fotiadis, D. I. (2023) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2023.3283149 · Cited by 95 · see citing works
Artificial intelligence in nutrition research: perspectives on current and future applications
Côté, M., & Lamarche, B. (2021) · Applied Physiology, Nutrition, and Metabolism · doi:10.1139/apnm-2021-0448 · Cited by 94 · see citing works
Artificial intelligence, nutrition, and ethical issues: A mini-review
Detopoulou, P., Voulgaridou, G., Moschos, P. N., Levidi, D., Anastasiou, T., Dedes, V., Diplari, E. M., Fourfouri, N., Giaginis, C., Panoutsopoulos, G. I., et al. (2023) · Clinical Nutrition Open Science · doi:10.1016/j.nutos.2023.07.001 · Cited by 94 · see citing works
What is Artificial Intelligence?
Morandín-Ahuerma, F. (2022) · International Journal of Research Publication and Reviews · doi:10.55248/gengpi.2022.31261 · Cited by 89 · see citing works
Artificial intelligence & clinical nutrition: What the future might have in store
Bond, A., Mccay, K., & Lal, S. (2023) · Clinical Nutrition ESPEN · doi:10.1016/j.clnesp.2023.07.082 · Cited by 79 · see citing works
Editorial: What is Artificial Intelligence?
Abbass, H. A. (2021) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2021.3096243 · Cited by 72 · see citing works
Salinari, A., Machì, M., Díaz, Y. A., Cianciosi, D., Qi, Z., Yang, B., Cotorruelo, M. S. F., Villar, S. G., López, L. A. D., Battino, M. A., et al. (2023) · Diseases · doi:10.3390/diseases11030097 · Cited by 71 · see citing works
Digital disruption of dietetics: are we ready?
Kelly, J. T., Collins, P. F., McCamley, J., Ball, L., Roberts, S., & Campbell, K. L. (2020) · Journal of Human Nutrition and Dietetics · doi:10.1111/jhn.12827 · Cited by 66 · see citing works
Davis, C. R., Murphy, K. J., Curtis, R., & Maher, C. A. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17239137 · Cited by 61 · see citing works
Diet Adherence among Adults with Type 2 Diabetes Mellitus: A Concept Analysis
Salmi, N. A., Cook, P. F., & D’Souza, M. S. (2021) · Oman Medical Journal · doi:10.5001/omj.2021.69 · Cited by 60 · see citing works
Artificial Emotional Intelligence: Conventional and deep learning approach
Kumar, H., & Martín, A. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118651 · Cited by 51 · see citing works
Machine learning modeling practices to support the principles of AI and ethics in nutrition research
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) · Nutrition & Diabetes · doi:10.1038/s41387-022-00226-y · Cited by 48 · see citing works
Artificial Intelligence: Distinguishing Between Types & Definitions
Martinez, R. (2019) · Scholarly Commons - UNLV Boyd Law (University of Nevada, Las Vegas) · Cited by 46 · see citing works
What Makes Nutrition Research So Difficult to Conduct and Interpret?
Vitolins, M. Z., & Case, T. (2020) · Diabetes Spectrum · doi:10.2337/ds19-0077 · Cited by 45 · see citing works
Papathanail, I., Brühlmann, J., Vasiloglou, M. F., Stathopoulou, T., Exadaktylos, A. K., Stanga, Z. G., Münzer, T., & Mougiakakou, S. G. (2021) · Nutrients · doi:10.3390/nu13124539 · Cited by 42 · see citing works
Folson, G. K., Bannerman, B., Atadze, V., Ador, G., Kolt, B., McCloskey, P., Gangupantulu, R., Arrieta, A., Braga, B. C., Arsenault, J. E., et al. (2023) · Journal of Nutrition · doi:10.1016/j.tjnut.2023.06.001 · Cited by 39 · see citing works
Detecting race and gender bias in visual representation of AI on web search engines
Makhortykh, M., Urman, A., & Ulloa, R. (2021) · BORIS (University Library Bern) · Cited by 38 · see citing works
Joiner, I. A. (2018) · Elsevier eBooks · doi:10.1016/b978-0-08-102253-5.00002-2 · Cited by 38 · see citing works
Human-Mimetic Estimation of Food Volume from a Single-View RGB Image Using an AI System
Yang, Z., Yu, H., Cao, S., Jia, W., Xu, Q., Yuan, D., Zhang, H., Mao, Z., & Sun, M. (2021) · Electronics · doi:10.3390/electronics10131556 · Cited by 37 · see citing works
Detecting race and gender bias in visual representation of AI on web search engines
Makhortykh, M., Urman, A., & Ulloa, R. (2021) · International Workshop on Algorithmic Bias in Search and Recommendation · doi:10.1007/978-3-030-78818-6_5 · Cited by 37 · see citing works
Sefa-Yeboah, S. M., Annor, K. O., Koomson, V. J., Saalia, F. K., Steiner‐Asiedu, M., & Mills, G. A. (2021) · International Journal of Telemedicine and Applications · doi:10.1155/2021/6624057 · Cited by 36 · see citing works
Chen, A. M. H., Draime, J. A., Berman, S., Gardner, J. C., Krauss, Z., & Martinez, J. (2022) · Exploratory Research in Clinical and Social Pharmacy · doi:10.1016/j.rcsop.2022.100129 · Cited by 35 · see citing works
Protecting patient safety and privacy in the era of artificial intelligence
Alonso, A. G., & Siracuse, J. J. (2023) · Seminars in Vascular Surgery · doi:10.1053/j.semvascsurg.2023.06.002 · Cited by 34 · see citing works
Predicting malnutrition from longitudinal patient trajectories with deep learning
Jin, B. T., Choi, M. H., Moyer, M. F., & Kim, D. A. (2022) · PLoS ONE · doi:10.1371/journal.pone.0271487 · Cited by 31 · see citing works
Shanklin, R., Samorani, M., Harris, S. L., & Santoro, M. A. (2022) · Philosophy & Technology · doi:10.1007/s13347-022-00590-8 · Cited by 30 · see citing works
Morgenstern, J., Rosella, L. C., Costa, A. P., & Anderson, L. N. (2022) · Applied Physiology, Nutrition, and Metabolism · doi:10.1139/apnm-2021-0502 · Cited by 27 · see citing works
Taylor, S., Korpusik, M., Das, S., Gilhooly, C., Simpson, R., Glass, J., & Roberts, S. (2021) · Journal of Medical Internet Research · doi:10.2196/26988 · Cited by 26 · see citing works
Machine learning approach to predict body weight in adults
Fujihara, K., Yamada, M. H., Horikawa, C., Iwanaga, M., Tanaka, H., Nomura, H., Sui, Y., Tanabe, K., Yamada, T., Kodama, S., et al. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1090146 · Cited by 24 · see citing works
Maharjan, B., Li, J., Kong, J., & Tao, C. (2019) · International Conference on e-Health Networking, Applications and Services · doi:10.1109/healthcom46333.2019.9009613 · Cited by 22 · see citing works
Wymelbeke-Delannoy, V. V., Juhel, C., Bole, H., Sow, A.-K., Guyot, C., Belbaghdadi, F., Brousse, O., & Paindavoine, M. (2022) · Nutrients · doi:10.3390/nu14010221 · Cited by 19 · see citing works
Artificial Intelligence Applications to Public Health Nutrition
An, R., & Wang, X. (2023) · Nutrients · doi:10.3390/nu15194285 · Cited by 18 · see citing works
Data Collection Theory in Healthcare Research: The Minimum Dataset in Quantitative Studies
Kwok, C. S., Muntean, E.-A., Mallen, C. D., & Borovac, J. A. (2022) · Clinics and Practice · doi:10.3390/clinpract12060088 · Cited by 17 · see citing works
Key Factors and AI-Based Risk Prediction of Malnutrition in Hospitalized Older Women
Larburu, N., Artola, G., Kerexeta, J., Caballero, M., Ollo, B., & Lando, C. M. (2022) · Geriatrics · doi:10.3390/geriatrics7050105 · Cited by 16 · see citing works
Roper, J. R., Lin, M., & Rong, Y. (2022) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.13873 · Cited by 14 · see citing works
Beyeler, M., Légeret, C., Kiwitz, F., & van der Horst, K. (2023) · JMIR Human Factors · doi:10.2196/47913 · Cited by 14 · see citing works
Chen, X., Johnson, E., Kulkarni, A., Ding, C., Ranelli, N., Chen, Y., & Xu, R. (2021) · Nutrients · doi:10.3390/nu13114132 · Cited by 13 · see citing works
Artificial Intelligence (AI) based Nutrition Advisorusing an App
Murumkar, A. D., Singh, A., Chachar, B. R., Bagade, P. D., & Zaware, G. (2023) · International Conference on Sustainable Computing and Smart Systems (ICSCSS) · doi:10.1109/icscss57650.2023.10169703 · Cited by 12 · see citing works
Artificial intelligence for the practical assessment of nutritional status in emergencies
Watkins, B., Odallo, L., & Yu, J. (2024) · Expert Systems · doi:10.1111/exsy.13550 · Cited by 9 · see citing works
Flagged first, then by citations
1 of the 64 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- [43]Nunes-Galbes N.M. Systematic Reviews in Nutrition Using Artificial Intelligence Tools: A Brief Review Faculty of Public Health, University of São Paulo São Paulo, Brazil 2022
Cited by
Sorted by citations
Investigation and Assessment of AI’s Role in Nutrition—An Updated Narrative Review of the Evidence
Kassem, H., Beevi, A., Basheer, S., Elkabat, G., Ismail, L. C., & Papandreou, D. (2025) · Nutrients · doi:10.3390/nu17010190 · Cited by 116 · see citing works
A Scoping Review of Artificial Intelligence for Precision Nutrition
Wu, X., Oniani, D., Shao, Z., Arciero, P. J., Sivarajkumar, S., Hilsman, J., Mohr, A. E., Ibe, S., Moharir, M., Li, L.-J., et al. (2025) · Advances in Nutrition · doi:10.1016/j.advnut.2025.100398 · Cited by 89 · see citing works
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
Healthy Food Recommendations for User Communities through Trust and Popularity-Aware Aggregation
Raghunathareddy, M., V, Basavaraja, P. H., Babu, J. A., Ramesh, G. P., & Prakash, R. (2025) · ITM Web of Conferences · doi:10.1051/itmconf/20257901044 · Cited by 56 · see citing works
Rouskas, K., Guela, M., Pantoura, M., Pagkalos, I. G., Hassapidou, M. N., Lalama, E., Pfeiffer, A. F. H., Decorte, E., Cornelissen, V., Wilson-Barnes, S., et al. (2025) · Nutrients · doi:10.3390/nu17071260 · Cited by 51 · see citing works
Diet Quality and Caloric Accuracy in AI-Generated Diet Plans: A Comparative Study Across Chatbots
Kaçar, H. K., Kaçar, Ö. F., & Avery, A. (2025) · Nutrients · doi:10.3390/nu17020206 · Cited by 48 · see citing works
Donovan, S. M., Abrahams, M., Anthony, J. C., Bao, Y., Barragán, M., Bermingham, K. M., Blander, G., Keck, A.-S., Lee, B. Y., Nieman, K. M., et al. (2025) · Critical Reviews in Food Science and Nutrition · doi:10.1080/10408398.2025.2461237 · Cited by 44 · see citing works
Theodorakis, N., Kreouzi, M., Pappas, A., & Nikolaou, M. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms252413438 · Cited by 43 · see citing works
Smart nutrition: AI and 3D printing for personalized diets
Bhuiyan, M. N. I., & Nahid, M. (2025) · Food Nutrition · doi:10.1016/j.fnutr.2025.100032 · Cited by 33 · see citing works
Petkoska, A. T., Ognenoska, V., & Trajkovska‐Broach, A. (2025) · Sustainability · doi:10.3390/su17094187 · Cited by 29 · see citing works
Adilmetova, G., Nassyrov, R., Meyerbekova, A., Karabay, A., Varol, H. A., & Chan, M. (2024) · Journal of Nutrition · doi:10.1016/j.tjnut.2024.12.018 · Cited by 24 · see citing works
Arslan, N., Esin, K., & Ayyıldız, F. (2025) · BMC Public Health · doi:10.1186/s12889-025-22507-8 · Cited by 22 · see citing works
Strategies for preventing bone loss in populations with insufficient calcium and vitamin D intake
Yeum, K., Ju, S.-Y., & Choe, U. (2025) · Nutrition Research and Practice · doi:10.4162/nrp.2025.19.2.155 · Cited by 21 · see citing works
O USO DA INTELIGÊNCIA ARTIFICIAL NO ATENDIMENTO NUTRICIONAL: AVANÇOS E PERSPECTIVAS
Santos, S. O., Teixeira, M. N. R., Pinheiro, N. D. C., Souza, Á. P. S., & Oliveira, D. G. D. (2025) · REVISTA ERR01 · doi:10.56238/err01v10n4-022 · Cited by 19 · see citing works
Palomares, S. M., Ferrara, G., Sguanci, M., Gazineo, D., Godino, L., Palmisano, A., Paderno, A., Vrenna, G., Faraglia, E., Petrelli, F., et al. (2026) · Journal of Renal Nutrition · doi:10.1053/j.jrn.2025.06.002 · Cited by 17 · 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
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
Generative AI in Precision Nutrition: A Review of Current Developments and Future Directions
Rahman, L. A., Dedousis, V., Papathanail, I., Poursoleymani, R., Kafyra, M., Kalafati, I. P., & Mougiakakou, S. G. (2026) · Nutrients · doi:10.3390/nu18060938 · Cited by 14 · see citing works
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
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
Smart Kitchens of the Future: Technology’s Role in Food Safety, Hygiene, and Culinary Innovation
Anumudu, C. K., Augustine, J. A., Uhegwu, C. C., Nzube, U. J., Ugwoegbu, M. O., Shodeko, O. R., & Onyeaka, H. N. (2025) · Standards · doi:10.3390/standards5030021 · Cited by 11 · 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
Amil, S., Gagnon, M., Bédard, A., Da, S. M. A. R., Mora, D. Z., Drapeau, V., & Desroches, S. (2025) · Journal of Medical Internet Research · doi:10.2196/78220 · Cited by 8 · see citing works
Dhakal, M., Banerjee, M., Pandey, K. K., & Seniya, C. (2026) · Discover Food · doi:10.1007/s44187-025-00777-w · Cited by 8 · see citing works
Blendea, L., Balmuș, I.-M., Petroaie, A. D., Novac, O., Novac, B., Gurzu, I. L., Trifan, A. V., Csabai, J., Miler, A. A., & Timofte, D. V. (2025) · BRAIN BROAD RESEARCH IN ARTIFICIAL INTELLIGENCE AND NEUROSCIENCE · doi:10.70594/brain/16.s1/15 · Cited by 7 · see citing works
Raghunathan, K., Morris, M. E., Wani, T. A., Edvardsson, K., Peiris, C. L., Fowler‐Davis, S., McKercher, J. P., Bourke, S. L., Danish, S., Johnston, J. H., et al. (2025) · BMJ Open · doi:10.1136/bmjopen-2024-098290 · Cited by 7 · see citing works
Mphasha, M. H., & Mothiba, T. M. (2025) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph22071047 · Cited by 7 · see citing works
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
An Efficient Algorithm for Sorting and Duplicate Elimination by Using Logarithmic Prime Numbers
Yeh, W., & Forghani-elahabad, M. (2024) · Big Data and Cognitive Computing · doi:10.3390/bdcc8090096 · Cited by 6 · see citing works
Personalized Nutrition in Pediatric Chronic Diseases
Escobedo-Monge, M. F., Lustig, R. H., Suchkov, S. V., Blokh, S. P., Andronova, N., Goryacheva, O., Moyseyak, M. B., Vlasov, T. D., Solís, H. A., Polyakova, V., et al. (2025) · Metabolites · doi:10.3390/metabo15100653 · 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
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
Ruiz, F. E. A., Tejera, E., Liceaga, A. M., & Álvarez-Suárez, J. M. (2026) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2026.105594 · Cited by 5 · see citing works
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
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
Fatima, T., sajjad, I., Qamar, M., Afzal, M. A., Noor, N., Tariq, M. N., Yawar, D. A., Abbasi, A. J., Rahim, F., Farooq, M. U., et al. (2026) · Social science review archives. · doi:10.70670/sra.v4i2.2046 · Cited by 4 · see citing works
A Predictive Nutritional Assessment System for Vegetarians using Artificial Neural Networks
Akiotu, C. A., Cole, O. A., Bunmi, E. E., Temiola, T., Oladele, H. A., Oluwasegun, V. O., Oyedemi, O. A., & Matthew, U. O. (2025) · Journal of Future Artificial Intelligence and Technologies · doi:10.62411/faith.3048-3719-117 · Cited by 4 · see citing works
IGSMNet: Ingredient-Guided Semantic Modeling Network for Food Nutrition Estimation
Zhang, D., Shi, W., Ma, B., Min, W., & Wu, X. (2025) · Foods · doi:10.3390/foods14213697 · Cited by 3 · see citing works
The role of language technology and artificial intelligence in food security policymaking
Meeske, M., Cruijssen, F., van der Lee, C., & Krahmer, E. (2025) · Discover Sustainability · doi:10.1007/s43621-025-02209-2 · Cited by 3 · see citing works
Güler, Ü., Çelik, Ö. M., Başpınar, E. N., Değirmencioğlu, E., Eser, B., Serenli̇oğlu, D., Kiliç, G., Gençoğlu, İ., Kaya, M., & Sarıteke, Y. (2026) · BMC Public Health · doi:10.1186/s12889-026-27238-y · Cited by 3 · see citing works
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
Artificial intelligence adoption by allied health professionals: a scoping review
Morris, M. E., Raghunathan, K., Peiris, C. L., Gilmartin‐Thomas, J., Heng, H. W. F., McKercher, J. P., Edvardsson, K., Ho, K. H. M., Johnston, J., Bourke, S., et al. (2026) · European Journal of Physiotherapy · doi:10.1080/21679169.2026.2665430 · Cited by 3 · see citing works
Galali, Y., Hadi, P. A. A., Hassan, A. M. A., Balaky, H. H., Rashid, T. S., Sadee, B. A., Salih, T. S., & Hassanzadeh, H. (2026) · eFood · doi:10.1002/efd2.70165 · Cited by 3 · see citing works
Mai, A., Luan, Y., Loy, S. L., Qin, C., Ji, H., Obeng, A., Oladuti, M., Ejima, K., Mok, K. T., Kaur, S., et al. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1805704 · Cited by 3 · see citing works
Woodman, R. J., Mangoni, A. A., Cohen‐Woods, S., Mori, T. A., Beilin, L. J., Murphy, K. J., & Hodgson, J. M. (2025) · Nutrients · doi:10.3390/nu17050869 · Cited by 3 · see citing works
Alkan, Ş. B., & Demet, E. D. Ö. (2025) · Nutrition and Cancer · doi:10.1080/01635581.2025.2499160 · Cited by 3 · see citing works
Twenty years of behavioral nutrition– A reflection on the road less travelled
Lien, N., Clare Collins, A. O., Hingle, M. D., & Rosenkranz, R. R. (2025) · International Journal of Behavioral Nutrition and Physical Activity · doi:10.1186/s12966-025-01804-w · Cited by 3 · see citing works
Souza, L. L., da Mota, J. C. N. L., Carvalho, L. M., Ribeiro, A., Caponi, C. A., Pinhel, M. A. S., Costa‐Fraga, N., Díaz‐Lagares, Á., Izquierdo, A. G., Nonino, C. B., et al. (2025) · Nutrients · doi:10.3390/nu17061086 · Cited by 2 · see citing works
Abdelhadi, N. N., & Jaddoua, S. M. (2025) · Journal of the Egyptian National Cancer Institute · doi:10.1186/s43046-025-00297-9 · Cited by 2 · see citing works
Streb, W., Olma, A., Pajor, M., Suchodolski, A., Staśkiewicz‐Bartecka, W., Stanjek-Cichoracka, A., Mitręga, K. A., Kowalczyk, J. P., & Kalarus, Z. F. (2025) · Nutrients · doi:10.3390/nu17142263 · Cited by 2 · see citing works
Patel, R. H., Shukla, S., Patel, N. A., Sharma, P., Mevada, S., Singh, S., & Prajapati, B. G. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-43823-3.00009-1 · Cited by 2 · see citing works
Adiyan, N. N., & Lule, N. O. (2026) · BMC Medical Education · doi:10.1186/s12909-026-09506-y · Cited by 2 · see citing works
KHAYRULLIN, R. N., Badamshina, G. G., Хафизов, А. Р., Makulova, L. Z., & Chukhlovina, Y. V. (2026) · Health Risk Analysis · doi:10.21668/health.risk/2026.2.16 · Cited by 2 · see citing works
Artificial Intelligence Holds Promise for Transforming Public Health Nutrition
An, R., & Yang, Y. (2024) · Nutrients · doi:10.3390/nu16234034 · Cited by 2 · see citing works
FlexFlow- The Nutrients and Recommendation Application Using Machine Learning
Elavarasi, D., Kavitha, R., Jayabarathan, J. K., & Aanjankumar, S. (2024) · International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS) · doi:10.1109/icuis64676.2024.10867020 · Cited by 1 · see citing works
Comprehensive Analytical Study of Existing Malnutrition Prediction Models
Rani, V., & Rana, R. P. S. (2025) · International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) · doi:10.1109/icaiss61471.2025.11042152 · Cited by 1 · see citing works
Nolting, M., & Walsh, G. (2025) · International Journal of Intelligent Computing and Cybernetics · doi:10.1108/ijicc-03-2025-0122 · Cited by 1 · see citing works
Artificial intelligence in community medicine in India: The present and future!
Chugh, L. T., & Ravichandran, R. (2025) · The Journal of Clinical and Scientific Research · doi:10.4103/jcsr.jcsr_82_25 · Cited by 1 · see citing works
Thrive-Dine: A Machine Learning Powered Diet Recall App for Personalized Nutrition Management
Srivastava, A., Jain, A., Gupta, H. K. G. H. K., Tyagi, H., & Singh, G. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-20994-8_30 · Cited by 1 · see citing works
Kopuz, T. N. Y., Haydaroğlu, M., & Uçar, K. (2026) · Nutrition & Dietetics · doi:10.1111/1747-0080.70081 · Cited by 1 · see citing works
LEVERAGING BIG DATA FOR DOG HEALTH ANALYSIS: AN EXPLORATORY STUDY USING "TANBA" IN KAZAKHSTAN
Shoman, A., Smaiyl, A., Kalykulova, A., Nugumanova, A., & Mukhametkaliyev, A. (2025) · Scientific Journal of Astana IT University · doi:10.37943/21suas7119 · Cited by 1 · see citing works
Chronic Diseases and Public Health Nutrition Strategies
Tayebi-Khorrami, V., Fadaei, M. R., Rajabi, M. J., & Askari, V. R. (2025) · Springer eBooks · doi:10.1007/978-3-031-32047-7_61-1 · Cited by 1 · see citing works
Low, K. M. Y., & Ali, S. H. (2025) · Current Opinion in Clinical Nutrition & Metabolic Care · doi:10.1097/mco.0000000000001156 · Cited by 1 · see citing works
Li, S., & Ding, G. (2025) · Future in Educational Research · doi:10.1002/fer3.70033 · Cited by 1 · see citing works
Ajmera, K. M., Patel, O. P., & Shah, N. (2026) · Cureus · doi:10.7759/cureus.100976 · Cited by 1 · see citing works
Kaur, H., & Sharma, P. (2026) · Discover Public Health · doi:10.1186/s12982-026-01796-3 · Cited by 1 · see citing works
FoodCHA: Multi-Modal LLM Agent for Fine-Grained Food Analysis
Lee, W., Mekkoth, P., Tian, Y., Güngör, O., & Rosing, T. (2026) · International Symposium on Networks, Computers and Communications (ISNCC) · doi:10.1109/isncc70543.2026.11693901 · Cited by 1 · see citing works
Bhuiyan, M. N. I. (2026) · Food biomacromolecules. · doi:10.1002/fob2.70068 · Cited by 1 · see citing works
Revolutionizing Nutrition Care: The Role of Artificial Intelligence in Personalized Nutrition
Joshi, S., Dwivedi, A., Kumar, A., Aayush, K., Bisht, B., Kumar, S., & Kumar, V. (2026) · Journal of Food Science · doi:10.1111/1750-3841.71281 · Cited by 1 · see citing works
Multimodal information fusion for food nutrition estimation: A survey
Teng, X., Min, W., Liu, C., & Li, H. (2026) · Information Fusion · doi:10.1016/j.inffus.2026.104621 · Cited by 1 · see citing works
Stunting in Developing Countries
Okello, E., Mwangome, M., Kathembe, J., Tapkigen, J., Prentice, A. M., & Nabwera, H. (2025) · World review of nutrition and dietetics · doi:10.1159/000541846
The Complexities of Metabolic Flexibility and Precision Approaches to Sustainable Weight Management
Theodorakis, N., Kreouzi, M., Pappas, A., Feretzakis, G., Sakagianni, A., Kalodanis, K., Αναστασίου, Α., Nikolaou, M., & Koumpouros, Y. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-5787-3.ch005
Mekete, D., Toma, A., & Balcha, T. (2025) · Exploration of Medicine · doi:10.37349/emed.2025.1001328
Wang, Z. (2025) · Theoretical and Natural Science · doi:10.54254/2753-8818/2025.au23757
Dietify - AI Based Diet & Nutrition Consultation Application
Bhatt, A. (2025) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2025.72653
حسين, ع., سحنون, ا., & هواري, ب. (2025) · أطراس · doi:10.70091/atras/vol06no02.48
Empowering Individuals Through Personalized Risk Assessment and Nutrition Guidance
Kumar, P. S., Shruti, S., Sakshi, Y. P. A., & Karunya, K. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-5784-1_17
The Contribution of AI to Our Thinking of the Intersection of Longevity and Food Habits
MOSKOWITZ, H. R., & Wingert, S. (2025) · IntechOpen eBooks · doi:10.5772/intechopen.1011374
New Developments in Nutritional Neuroscience in the Age of Digitalization
Yıldırım, İ. (2025) · Nutritional neurosciences · doi:10.1007/978-981-95-1983-5_6
Neog, S. (2025) · International Conference on Circuits, Power and Intelligent Systems (CCPIS) · doi:10.1109/ccpis65231.2025.11234250
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
Silva, J. D. S., Silva, C. D. S., & Lepikson, H. A. (2025) · Caderno Pedagógico · doi:10.54033/cadpedv22n14-048
AI-Powered Precision Nutrition in Metabolic Syndrome Management
Nazneen, F., Ansari, A., & Ghosh, J. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-3196-6.ch005
Bone health in children and adolescents: impact of risk factors and modern preventive strategies
Бекетова, Г. В., Sharikadze, O. V., Balatska, N. I., & Солдатова, О. В. (2025) · CHILD`S HEALTH · doi:10.22141/2224-0551.20.8.2025.1932
Leveraging Artificial Intelligence for Precise Dietary Assessment Opportunities and Challenges
TEHSEEN, A., Ismail, H., Amjad, A., Ijaz, K., Aneeza, Yousaf, R. F., Amjad, M., Zahid, K., & Aslam, K. (2025) · Indus journal of bioscience research. · doi:10.70749/ijbr.v3i11.2731
Nutritional & Lifestyle Recommendations using an AI-Driven approach
Nithya Rubini, R., Afrin Banu, S., Sophia Eunice, K., Kathrine, G. J. W., Silas, S., & Stewart Kirubakaran, S. (2025) · International Conference on Applied Artificial Intelligence and Computing (ICAAIC) · doi:10.1109/icaaic64647.2025.11330438
Óptica y fotónica para la calidad de los alimentos y la nutrición
Istituto di Fisica Applicata Nello Carrara, C., & Righini, G. C. (2026) · Journal of Behavior and Feeding · doi:10.32870/jbf.v5i10.116
Machine Learning Approaches for Multi-Label Classification of Nutritional Content in Food Recipes
Anggraini, R. N. E., Sunaryono, D., & Santoso, M. A. A. (2025) · International Conference of Science and Information Technology in Smart Administration (ICSINTESA) · doi:10.1109/icsintesa68165.2025.11413689
Murofushi, K., Thomas, D., Dhakal, S., Park, H., Kleinberg, S., Salvia, M. G., Jessri, M., Kittrell, H. D., & Cataldo, A. V. R. (2026) · Journal of the Academy of Nutrition and Dietetics · doi:10.1016/j.jand.2026.156320
Murofushi, K., Thomas, D., Dhakal, S., Park, H., Kleinberg, S., Salvia, M. G., Jessri, M., Kittrell, H. D., & Cataldo, A. V. R. (2026) · American Journal of Clinical Nutrition · doi:10.1016/j.ajcnut.2026.101240
AI and IoT in Nutritional Science: Transforming Digestion Research and Precision Nutrition
Pushparaj, P., Mohan, L., Anju, K. A., Cherian, E., Rajan, R., & Kumar, N. S. (2026) · Current Research in Nutrition and Food Science Journal · doi:10.12944/crnfsj.14.1.5
When the Lunchbox Meets the Algorithm: AI’s Next Chapter in Children’s Nutrition Education
Lovan, P., Hasnin, S., Jeune, S. C., Larsen, L., & Fredericks, L. (2026) · Journal of Nutrition Education and Behavior · doi:10.1016/j.jneb.2026.03.005
Uca, E., & Sabuncular, G. (2026) · Food and Humanity · doi:10.1016/j.foohum.2026.101226
Elobeid, T., Muhaid, H., & Hamad, A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-40501-3.00003-0
Utilization of AI in diet–disease relationships
Singh, S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-40501-3.00018-2
Scope of artificial intelligence in 3D printing of food products
Brennan, C. S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-40501-3.00001-7
Sun, H., Liu, X., & Li, H. (2026) · Frontiers in Nutrition · doi:10.3389/fnut.2026.1821103
Piderit, M. C. (2026) · South African Journal of Clinical Nutrition · doi:10.1080/16070658.2026.2679826
Nordhagen, S., Beal, T., Beesabathuni, K., Ganguly, S., Knight, F., Koupparis, K., Lok, X. X. C., Mbuya, M. N. N., Parwal, A., & Tata, S. (2026) · doi:10.12688/verixiv.3283.1
- Authors
- Sosa-Holwerda, A., Park, O.-H., Albracht-Schulte, K., Niraula, S., Thompson, L., & Oldewage-Theron, W.
- Affiliations
- Texas Tech University (US)
- Department of Kinesiology & Sport Management, Texas Tech University, Lubbock, TX 79409, USA
- Corresponding author
- Oldewage-Theron, Wilna
- Venue
- Nutrients
- Publisher
- MDPI AG
- Published
- 2024-06-28
- Volume
- 16
- Issue
- 13
- Pages
- 2066
- ISSN
- 2072-6643 (ISSN-L)
- PMC
- PMC11243505
- OpenAlex
- W4400119754
- Cited by
- 114
- References
- 64
- Citations by year
- 2026: 50 · 2025: 58 · 2024: 6
- FWCI
- 2.60
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nutrients (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Nutrition, Genetics, and Disease
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence, nutrition research, dietary assessment, lifestyle intervention, diet-related diseases, AI ethics, artificial intelligence in healthcare
- MeSH terms
- Artificial Intelligence, Humans, Biomedical Research, Nutritional Sciences
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
