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

Innovations and Technology for Healthy Eating Education (487) Natural Language Processing (917) Mobile Health (mhealth) (3464) Instruments and Questionnaires for Nutrition and Food Intake (211) mHealth for Data Collection and Research (1196) mHealth for Wellness, Behavior Change and Prevention (3315) New Methods (1134) Machine Learning (2076)

Published on in Vol 23, No 12 (2021): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26988, first published .
Use of Natural Spoken Language With Automated Mapping of Self-reported Food Intake to Food Composition Data for Low-Burden Real-time Dietary Assessment: Method Comparison Study

Use of Natural Spoken Language With Automated Mapping of Self-reported Food Intake to Food Composition Data for Low-Burden Real-time Dietary Assessment: Method Comparison Study

Use of Natural Spoken Language With Automated Mapping of Self-reported Food Intake to Food Composition Data for Low-Burden Real-time Dietary Assessment: Method Comparison Study

Authors of this article:

Salima Taylor1 Author Orcid Image ;   Mandy Korpusik2 Author Orcid Image ;   Sai Das1 Author Orcid Image ;   Cheryl Gilhooly1 Author Orcid Image ;   Ryan Simpson3 Author Orcid Image ;   James Glass2 Author Orcid Image ;   Susan Roberts1 Author Orcid Image
Article Authors Cited by (11) Tweetations (5) Metrics

Journals

  1. Xing W, Gao W, Zhao Z, Xu X, Bu H, Su H, Mao G, Chen J. Dietary flavonoids intake contributes to delay biological aging process: analysis from NHANES dataset. Journal of Translational Medicine 2023;21(1) View
  2. Liu Y, Yin T, He M, Fang C, Peng S. The relationship of dietary flavonoids and periodontitis in US population: a cross-sectional NHANES analysis. Clinical Oral Investigations 2024;28(3) View
  3. Sosa-Holwerda A, Park O, Albracht-Schulte K, Niraula S, Thompson L, Oldewage-Theron W. The Role of Artificial Intelligence in Nutrition Research: A Scoping Review. Nutrients 2024;16(13):2066 View
  4. Wang S, Xiong F, Liu Y, Feng Z. Exploring flavonoid intake and all-cause mortality in diverse health conditions: Insights from NHANES 2007–2010 and 2017–2018. Nutrition 2024;127:112556 View
  5. Zheng J, Wang J, Shen J, An R. Artificial Intelligence Applications to Measure Food and Nutrient Intakes: Scoping Review. Journal of Medical Internet Research 2024;26:e54557 View
  6. Xiao P, Wang Z, Lu Z, Liu S, Huang C, Xu Y, Tian Y. The association between dietary flavonoid intake and bone mineral density and osteoporosis in US adults: data from NHANES 2007–2008, 2009–2010 and 2017–2018. BMC Public Health 2024;24(1) View
  7. Phalle A, Gokhale D. Navigating next-gen nutrition care using artificial intelligence-assisted dietary assessment tools—a scoping review of potential applications. Frontiers in Nutrition 2025;12 View
  8. Bayram H, Ozturkcan A. Applications of generative and predictive AI in nutrition and dietetics: a narrative review. Informatics for Health and Social Care 2025;50(3-4):133 View
  9. Mehta T, John T, El Zein A, Faught V, Nawshin T, Chilke T, Cohen C, Cherrington A, Thirumalai M. Gamified Optimized Diabetes Management With Artificial Intelligence–Powered Rural Telehealth Intervention (GODART): Protocol for an Optimization Pilot and Feasibility Trial. JMIR Research Protocols 2025;14:e70271 View
  10. Sui X, Liu Y, Zhao J, Wang Z, Zhang G, Alvarez-Suarez J. Dietary flavonoids may improve insulin resistance: NHANES, network pharmacological analyses and in vitro experiments. PLOS One 2025;20(12):e0338100 View

Books/Policy Documents

  1. Côté M, Lamarche B. Artificial Intelligence in Clinical Practice. View