This paper is in the following e-collection/theme issue:

Digital Health Reviews (1724) Instruments and Questionnaires for Nutrition and Food Intake (211) Innovations and Technology for Healthy Eating Education (487) Artificial Intelligence (2129) Applications of AI (287)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/54557, first published .
Artificial Intelligence Applications to Measure Food and Nutrient Intakes: Scoping Review

Artificial Intelligence Applications to Measure Food and Nutrient Intakes: Scoping Review

Artificial Intelligence Applications to Measure Food and Nutrient Intakes: Scoping Review

Authors of this article:

Jiakun Zheng1 Author Orcid Image ;   Junjie Wang2 Author Orcid Image ;   Jing Shen3 Author Orcid Image ;   Ruopeng An4 Author Orcid Image
Article Authors Cited by (23) Tweetations Metrics

Journals

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  12. Hildebrand N, Sier M, van Kuijk S, Hoeijmakers L, Ackermans L, Ubachs J, Stassen L, Ruber N, Schaghen-D’Antonio V, Goedegebuure E, Baade-Corpelijn L, Bongers B, Stoot J, Sosef M, Lambrechts S, de Vos van Steenwijk P, Engelen M, Lubbers T, Blokhuis T, Ten Bosch J, Valkenburg-van Iersel L, de Vos-Geelen J, den Dulk M, van Dijk D, Olde Damink S, Rensen S. Patient-recorded indexing measurements (PRIMS) – study protocol of a prospective observational cohort study to improve the accuracy of the diagnosis of cancer cachexia. BMC Cancer 2025;25(1) View
  13. Kalu K, Ataguba G, Onifade O, Orji F, Giweli N, Orji R. Application of Artificial Intelligence Technologies as an Intervention for Promoting Healthy Eating and Nutrition in Older Adults: A Systematic Literature Review. Nutrients 2025;17(20):3223 View
  14. Biró L. Artificial intelligence applications in nutrition science: A brief thematic overview. Acta Alimentaria 2025 View
  15. Avdeeva M, Gogoleva M, Rizakhanova O, Shatalova M, Nosacheva V, Sandgarten L, Signaevskaya A, Chestnyi I. Modern approaches to formation people eating behavior using digital technologies and artificial intelligence. Russian Journal of Preventive Medicine 2025;28(10):33 View
  16. Mancin S, Ferrara G, Lopane D, Cangelosi G, Petrelli F, Palomares S, Sguanci M. Artificial intelligence in the management of hospital malnutrition: A systematic review. Clinical Nutrition ESPEN 2025;70:542 View
  17. Rodríguez-Jiménez M, Martín-del-Campo-Becerra G, Sumalla-Cano S, Crespo-Álvarez J, Elio I. Image-Based Dietary Energy and Macronutrients Estimation with ChatGPT-5: Cross-Source Evaluation Across Escalating Context Scenarios. Nutrients 2025;17(22):3613 View
  18. Belkhouribchia J, Pen J. Artificial intelligence in clinical nutrition. A narrative review. Clinical Nutrition ESPEN 2025 View
  19. Cagiran Gorgin N, Cagiran Yilmaz F. Comparing Mediterranean, Dietary Phytochemical Index, and Healthy Eating Indices with a Theoretical Model of a Dental Diet Risk Score: An In-Silico Modeling Study. Cureus 2025 View
  20. Mercola J. Fatty liver reexamined choline and mitochondrial toxin amelioration. World Journal of Biological Chemistry 2025;16(4) View

Books/Policy Documents

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Conference Proceedings

  1. Shen A, Han X, Qiao X, Liu K, Sun J, Wang J. Proceedings of the 1st International Workshop on Multi-modal Food Computing. Food Nutrient Estimation Based on a Dual-directional Self-attention Aggregation Enhanced Inception Model View
  2. Arefeen A, Fessler S, Mostafavi S, Johnston C, Ghasemzadeh H. 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). MealMeter: Using Multimodal Sensing and Machine Learning for Automatically Estimating Nutrition Intake View