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) Obesity (117) mHealth for Data Collection and Research (1196)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/51432, first published .
Advancements in Using AI for Dietary Assessment Based on Food Images: Scoping Review

Advancements in Using AI for Dietary Assessment Based on Food Images: Scoping Review

Advancements in Using AI for Dietary Assessment Based on Food Images: Scoping Review

Authors of this article:

Phawinpon Chotwanvirat1, 2 Author Orcid Image ;   Aree Prachansuwan3 Author Orcid Image ;   Pimnapanut Sridonpai3 Author Orcid Image ;   Wantanee Kriengsinyos3 Author Orcid Image
Article Authors Cited by (19) Tweetations Metrics

Journals

  1. Mujica-Coopman M, Martínez-Arroyo A, Rebolledo N, Quintiliano-Scarpelli D, Reyes M. Development and feasibility of a 24-hour recall software to characterize the Chilean diet. Journal of Food Composition and Analysis 2025;144:107660 View
  2. Sozzi M, Cavallini N, Chiadò A, Gavoci G, Cantaluppi E, Haxhari F, Savorani F. A new versatile algorithm to extract particle’s features from FESEM images: method evaluation and a case study on rice kernels. Chemometrics and Intelligent Laboratory Systems 2025;263:105415 View
  3. Basso M, Zhang L, Savva G, Cohen Kadosh K, Traka M. Relative Validity of the Food Recording Smartphone App Libro in Young People Vulnerable to Eating Disorder: A Preliminary Cross-Over Study. Nutrients 2025;17(11):1823 View
  4. Gan T, Cheng H, Tse M, He N. Development of a nurse-led, evidence-based, and theory-informed dietary intervention to modify multiple unhealthy dietary behaviors in gastric cancer survivors. Asia-Pacific Journal of Oncology Nursing 2025;12:100739 View
  5. Revesai Z, Kogeda O. Lightweight Interpretable Deep Learning Model for Nutrient Analysis in Mobile Health Applications. Digital 2025;5(2):23 View
  6. Grootswagers P, Grootswagers T. Artificial intelligence in nutrition and ageing research – A primer on the benefits. Maturitas 2025;200:108662 View
  7. Portmann L, Fitzpatrick J, Halmos E, Bryant R, Day A. Looking Beyond Nutrients, How to Assess Diet Quality in an Inflammatory Bowel Disease Population—A Narrative Review. Nutrients 2025;17(14):2343 View
  8. Lambert K, Anderson C, Ibrahim Cheikh Hassan H, Mullan A, Sabatino A, Shroff R. Practical Guide to Nutritional Assessment in CKD. Clinical Journal of the American Society of Nephrology 2025;20(11):1610 View
  9. Hsuan C, Lee Y, Hsu H, Ouyang C, Yeh W, Tang W. Comparison of Accuracy in the Evaluation of Nutritional Labels on Commercial Ready-to-Eat Meal Boxes Between Professional Nutritionists and Chatbots. Nutrients 2025;17(19):3044 View
  10. Piazza C, Laesser C, Kastrati L, Barnard-Kelly K, Nakas C, Herzig D, Bally L. App-based automated meal analysis in adults with type 1 diabetes using automated insulin delivery: a randomized controlled trial. eClinicalMedicine 2025;89:103537 View
  11. Zhu R, Dong Y, Guo J, He J, Chen H, Wang R, Ren F, Raben A, Martinez J. From prediction of personalized metabolic responses to foods to computational nutrition: Concepts of an emerging interdisciplinary field. Clinical Nutrition 2025;55:11 View
  12. Chuang Y, Chang R, Cheng Y, Huang S, Yu T, Shu K. Generative-AI Based Health Advisory System for Patients with Chronic Diseases. Mobile Networks and Applications 2025 View
  13. Loomba M, Bansal S, Singh K, Mishra P, Ghosh S, Raghunath M, Mishra A, Sinha J. The Diet–Obesity–Brain Axis: Metabolic, Epigenetic, and DNA-Repair Pathways Linking Eating Patterns to Cognitive Aging, with an AI-Enabled Translational Perspective. Nutrients 2025;17(21):3493 View
  14. Wibowo J, Tianto E, Wahyudi A, Edbert I. Benchmarking Segmentation Models for Food Image Recognition. Procedia Computer Science 2025;269:1289 View
  15. Yamanaka S, Yokoyama Y, Kurotani K. Changes in Dietary Survey Methods in Research on the “Japanese Diet”. The Japanese Journal of Nutrition and Dietetics 2025;83(Supplement):S75 View

Books/Policy Documents

  1. Díaz-Trujillo L, Hernández-Romero I, Ríos-Fuentes B, Hernández-Navarro C, Fuentes-Cortés L. Artificial Intelligence for the Water-Energy-Food Nexus. View

Conference Proceedings

  1. Khoo W, Hsu L, Ramirez F, Huang L, Konkimalla T, Chung C, Crandall D. Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems. Bridging Human Intuition and AI in Colorful Food Assessment View
  2. Khlaisamniang P, Kerdthaisong K, Vorathammathorn S, Yongsatianchot N, Phimsiri H, Chinkamol A, Thitseesaeng T, Veerakanjana K, Kachai K, Ittichaiwong P, Saengja T. 2025 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Decomposing Food Images for Better Nutrition Analysis: a Nutritionist-Inspired Two-Step Multimodal Llm Approach View
  3. Chuang Y, Cheng Y, Yu T, Chang R, Chin H, Deng D. 2025 21th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob). Generative-AI Based Health Management System for CKD Patients View