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Navigating next-gen nutrition care using artificial intelligence-assisted dietary assessment tools-a scoping review of potential applications

Phalle, Anuja, Gokhale, Devaki·2025·Frontiers in Nutrition
DOI 10.3389/fnut.2025.1518466
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Abstract

Introduction: Recent developments in Artificial Intelligence (AI) and Machine Learning (ML) technologies have opened new avenues for their applications in dietary assessments. Conventional dietary assessment methods are time-consuming, labor-driven, and have high recall bias. AI-assisted tools can be user-friendly and provide accurate dietary data. Hence, this review aimed to explore the applications of AI-assisted dietary assessment tools in real-world settings that could potentially enhance Next-Gen nutrition care delivery. Materials and methods: A total of 17,613 original, full-text articles using keywords such as “artificial intelligence OR food image analysis OR wearable devices AND dietary OR nutritional assessment,” published in English between January 2014 and September 2024 were extracted from Scopus, Web of Science, and PubMed databases. All studies exploring applications of AI-assisted dietary assessment tools with human participation were included; While methodological/developmental research and studies without human participants were excluded as this review specifically aimed to explore their applications in real-world scenarios for clinical purposes. In the final phase of screening, 66 articles were reviewed that matched our inclusion criteria and the review followed PRISMA-ScR reporting guidelines. Results: We observed that existing AI-assisted dietary assessment tools are integrated with mobile/web-based applications to provide a user-friendly interface. These tools can broadly be categorized as “Image-based” and “Motion sensor-based.” Image-based tools allow food recognition, classification, food volume/weight, and nutrient estimation whereas, Motion sensor-based tools help capture eating occasions through wrist movement, eating sounds, jaw motion & swallowing. These functionalities capture the dietary data regarding the type of food or beverage consumed, calorie intake, portion sizes, frequency of eating, and shared eating occasions as real-time data making it more accurate as against conventional dietary assessment methods. Dietary assessment tools integrated with AI and ML could estimate real-time energy and macronutrient intake in patients with chronic conditions such as obesity, diabetes, and dementia. Additionally, these tools are non-laborious, time-efficient, user-friendly, and provide fairly accurate data free from recall/reporting bias enabling clinicians to offer personalized nutrition. Conclusion: Therefore, integrating AI-based dietary assessment tools will help improve the quality of nutrition care and navigate next-gen nutrition care practices. More studies are required further to evaluate the efficacy and accuracy of these tools.

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  1. Gnagnarella, 2022, Telenutrition: changes in professional practice and in the nutritional assessments of Italian dietitian nutritionists in the COVID-19 era, Nutrients, 14, 1359, 10.3390/nu14071359 · DOI
  2. Farid, 2020, COVID-19 and telenutrition: remote consultation in clinical nutrition practice, Curr Dev Nutr, 4, nzaa124, 10.1093/cdn/nzaa124 · DOI
  3. Miyazawa, 2022, Artificial intelligence in food science and nutrition: a narrative review, Nutr Rev, 80, 2288, 10.1093/nutrit/nuac033 · DOI · Paper page
  4. Lewis, 2022, Nutrition care process quality evaluation and standardization tool: the next frontier in quality evaluation of documentation, J Acad Nutr Diet, 122, 650, 10.1016/j.jand.2021.07.004 · DOI
  5. Dao, 2019, Dietary assessment toolkits: an overview, Public Health Nutr, 22, 404, 10.1017/S1368980018002951 · DOI · Paper page
  6. Baghlaf, 2022, Traditional dietary assessment tools verses technology-based methods in children and adolescence. An updated review, Prog Nutr, 24, e2022017, 10.23751/pn.v24i1.11107 · DOI
  7. Dalakleidi, 2022, Applying image-based food-recognition systems on dietary assessment: a systematic review, Adv Nutr, 13, 2590, 10.1093/advances/nmac078 · DOI
  8. Andersen, 2021, How digital food affects our analog lives: the impact of food photography on healthy eating behavior, Front Psychol, 12, 634261, 10.3389/fpsyg.2021.634261 · DOI
  9. Osooli, 2019, Identifying critically ill patients at risk of malnutrition and underfeeding: a prospective study at an academic hospital, Adv Pharm Bull, 9, 314, 10.15171/apb.2019.037 · DOI
  10. Lee, 2019, Determination of nutrition risk and status in critically ill patients: what are our considerations?, Nutr Clin Pract, 34, 96, 10.1002/ncp.10214 · DOI
  11. Ferguson, 2023, Dietary assessment methods for measurement of oral intake in acute care and critically ill hospitalized patients: a scoping review, Nutr Res Rev, 1, 10.1017/S0954422423000288 · DOI
  12. Russo, 2022, Prospects and pitfalls of machine learning in nutritional epidemiology, Nutrients, 14, 1705, 10.3390/nu14091705 · DOI
  13. Salinari, 2023, The application of digital technologies and artificial intelligence in healthcare: an overview on nutrition assessment, Diseases, 11, 97, 10.3390/diseases11030097 · DOI
  14. Boushey, 2017, New mobile methods for dietary assessment: review of image-assisted and image-based dietary assessment methods, Proc Nutr Soc, 76, 283, 10.1017/S0029665116002913 · DOI · Paper page
  15. Hossin, 2015, A review on evaluation metrics for data classification evaluations, Int J Data Min Knowl Manage Process, 5, 01, 10.5121/ijdkp.2015.5201 · DOI · Paper page
  16. Wang, 2022, Enhancing nutrition care through real-time, sensor-based capture of eating occasions: a scoping review, Front Nutr, 9, 852984, 10.3389/fnut.2022.852984 · DOI
  17. Walker, 2018, The validity of dietary assessment methods to accurately measure energy intake in children and adolescents who are overweight or obese: a systematic review, Eur J Clin Nutr, 72, 185, 10.1038/s41430-017-0029-2 · DOI
  18. Burrows, 2010, A systematic review of the validity of dietary assessment methods in children when compared with the method of doubly labeled water, J Am Diet Assoc, 110, 1501, 10.1016/j.jada.2010.07.008 · DOI · Paper page
  19. Fialkowski, 2022, An active image-based Mobile food record is feasible for capturing eating occasions among infants ages 3–12 months old in Hawai ‘i, Nutrients, 14, 1075, 10.3390/nu14051075 · DOI
  20. Johansson, 2018, Active image-assisted food records in comparison to regular food records: a validation study against doubly labeled water in 12-month-old infants, Nutrients, 10, 1904, 10.3390/nu10121904 · DOI
  21. Wang, 2019, Evaluation of a technological image-based dietary assessment tool for children during pubertal growth: a pilot study, Nutrients, 11, 2527, 10.3390/nu11102527 · DOI
  22. Marcano-Olivier, 2019, Measuring lunchtime consumption in school cafeterias: a validation study of the use of digital photography, Public Health Nutr, 22, 1745, 10.1017/S136898001900048X · DOI
  23. Folson, 2023, Validation of mobile artificial intelligence technology–assisted dietary assessment tool against weighed records and 24-hour recall in adolescent females in Ghana, J Nutr, 153, 2328, 10.1016/j.tjnut.2023.06.001 · DOI
  24. Ptomey, 2015, Digital photography improves estimates of dietary intake in adolescents with intellectual and developmental disabilities, Disabil Health J, 8, 146, 10.1016/j.dhjo.2014.08.011 · DOI
  25. Beltran, 2018, Dietary assessment with a wearable camera among children: feasibility and intercoder reliability, J Acad Nutr Diet, 118, 2144, 10.1016/j.jand.2018.05.013 · DOI
  26. Jobarteh, 2023, Evaluation of acceptability, functionality, and validity of a passive image-based dietary intake assessment method in adults and children of Ghanaian and Kenyan origin living in London, UK, Nutrients, 15, 4075, 10.3390/nu15184075 · DOI
  27. Zhou, 2019, The use of wearable cameras in assessing children's dietary intake and behaviours in China, Appetite, 139, 1, 10.1016/j.appet.2019.03.032 · DOI
  28. Raber, 2018, Utility of eButton images for identifying food preparation behaviors and meal-related tasks in adolescents, Nutr J, 17, 1, 10.1186/s12937-018-0341-2 · DOI
  29. Veatupu, 2019, Me’akai in Tonga: exploring the nature and context of the food Tongan children eat in Ha’apai using wearable cameras, Int J Environ Res Public Health, 16, 1681, 10.3390/ijerph16101681 · DOI
  30. Kamar, 2019, Factors influencing British adolescents’ intake of whole grains: a pilot feasibility study using sensecam assisted interviews, Nutrients, 11, 2620, 10.3390/nu11112620 · DOI
  31. Braga, 2024, Feasibility of using an artificial intelligence-based telephone application for dietary assessment and nudging to improve the quality of food choices of female adolescents in Vietnam: evidence from a randomized pilot study, Curr Dev Nutr, 8, 102063, 10.1016/j.cdnut.2023.102063 · DOI
  32. Nguyen, 2022, Relative validity of a mobile AI-technology–assisted dietary assessment in adolescent females in Vietnam, Am J Clin Nutr, 116, 992, 10.1093/ajcn/nqac216 · DOI
  33. Serra, 2023, Assessing daily energy intake in adult women: validity of a food-recognition mobile application compared to doubly labelled water, Front Nutr, 10, 1255499, 10.3389/fnut.2023.1255499 · DOI
  34. Ashman, 2017, Validation of a smartphone image-based dietary assessment method for pregnant women, Nutrients, 9, 73, 10.3390/nu9010073 · DOI
  35. Ding, 2021, Evaluation of a novel WeChat applet for image-based dietary assessment among pregnant women in China, Nutrients, 13, 3158, 10.3390/nu13093158 · DOI
  36. McCloskey, 2019, Beyond nutrient intake: use of digital food photography methodology to examine family dinnertime, J Nutr Educ Behav, 51, 547, 10.1016/j.jneb.2019.01.020 · DOI
  37. Bulungu, 2021, Validation of a life-logging wearable camera method and the 24-h diet recall method for assessing maternal and child dietary diversity, Br J Nutr, 125, 1299, 10.1017/S0007114520003530 · DOI
  38. Bulungu, 2023, Automated wearable cameras for improving recall of diet and time use in Uganda: a cross-sectional feasibility study, Nutr J, 22, 7, 10.1186/s12937-022-00828-3 · DOI
  39. Locks, 2022, Assessing the diets of young children and adolescents in India: challenges and opportunities, Front Pediatr, 10, 725812, 10.3389/fped.2022.725812 · DOI
  40. Kant, 2015, 40-year trends in meal and snack eating behaviors of American adults, J Acad Nutr Diet, 115, 50, 10.1016/j.jand.2014.06.354 · DOI · Paper page
  41. Poobalan, 2014, Diet behaviour among young people in transition to adulthood (18–25 year olds): a mixed method study, Health Psychol Behav Med Open Access J, 2, 909, 10.1080/21642850.2014.931232 · DOI
  42. Larke, 2023, Surveying nutrient assessment with photographs of meals (SNAPMe): a benchmark dataset of food photos for dietary assessment, Nutrients, 15, 4972, 10.3390/nu15234972 · DOI
  43. Lucassen, 2021, The accuracy of portion size estimation using food images and textual descriptions of portion sizes: an evaluation study, J Hum Nutr Diet, 34, 945, 10.1111/jhn.12878 · DOI
  44. Radtke, 2023, Validation of diet ID™ in predicting nutrient intake compared to dietary recalls, skin carotenoid scores, and plasma carotenoids in university students, Nutrients, 15, 409, 10.3390/nu15020409 · DOI
  45. Chan, 2020, Validity and acceptability of image-based food record in assessing nutrient intake among selected Malaysian undergraduates, Malay J Med Health Sci, 16
  46. Ji, 2020, Validity and usability of a smartphone image-based dietary assessment app compared to 3-day food diaries in assessing dietary intake among Canadian adults: randomized controlled trial, JMIR Mhealth Uhealth, 8, e16953, 10.2196/16953 · DOI
  47. Naaman, 2021, Assessment of dietary intake using food photography and video recording in free-living young adults: a comparative study, J Acad Nutr Diet, 121, 749, 10.1016/j.jand.2020.09.040 · DOI
  48. Papathanail, 2023, The nutritional content of meal images in free-living conditions—automatic assessment with goFOODTM, Nutrients, 15, 3835, 10.3390/nu15173835 · DOI
  49. Qiu, 2020, Counting bites and recognizing consumed food from videos for passive dietary monitoring, IEEE J Biomed Health Inform, 25, 1471, 10.1109/JBHI.2020.3022815 · DOI
  50. Tufano, 2024, Rule-based systems to automatically count bites from meal videos, Front Nutr, 11, 1343868, 10.3389/fnut.2024.1343868 · DOI
  51. Kong, 2023, MyDietCam: development and usability study of a food recognition integrated dietary monitoring smartphone application, Digit Health, 9, 20552076221149320, 10.1177/20552076221149320 · DOI
  52. Harray, 2017, Perception v. actual intakes of junk food and sugar-sweetened beverages in Australian young adults: assessed using the mobile food record, Public Health Nutr, 20, 2300, 10.1017/S1368980017000702 · DOI
  53. Ziesemer, 2020, Occurrence of and reasons for “missing events” in mobile dietary assessments: results from three event-based ecological momentary assessment studies, JMIR Mhealth Uhealth, 8, e15430, 10.2196/15430 · DOI
  54. Papathanail, 2022, A feasibility study to assess Mediterranean diet adherence using an AI-powered system, Sci Rep, 12, 17008, 10.1038/s41598-022-21421-y · DOI
  55. Roux de Bézieux, 2021, Medical food assessment using a smartphone app with continuous glucose monitoring sensors: proof-of-concept study, JMIR Format Res, 5, e20175, 10.2196/20175 · DOI
  56. Gemming, 2015, Wearable cameras can reduce dietary under-reporting: doubly labelled water validation of a camera-assisted 24 h recall, Br J Nutr, 113, 284, 10.1017/S0007114514003602 · DOI
  57. Doulah, 2020, “Automatic ingestion monitor version 2”–a novel wearable device for automatic food intake detection and passive capture of food images, IEEE J Biomed Health Inform, 25, 568, 10.1109/JBHI.2020.2995473 · DOI
  58. Farooq, 2016, A novel wearable device for food intake and physical activity recognition, Sensors, 16, 1067, 10.3390/s16071067 · DOI
  59. Pan, 2022, Improvement of methodology for manual energy intake estimation from passive capture devices, Front Nutr, 9, 877775, 10.3389/fnut.2022.877775 · DOI
  60. Alshurafa, 2021, Association of number of bites and eating speed with energy intake: wearable technology results under free-living conditions, Appetite, 167, 105653, 10.1016/j.appet.2021.105653 · DOI
  61. Shen, 2016, Assessing the accuracy of a wrist motion tracking method for counting bites across demographic and food variables, IEEE J Biomed Health Inform, 21, 599, 10.1109/JBHI.2016.2612580 · DOI
  62. Wang, 2022, Wearable cameras reveal large intra-individual variability in timing of eating among young adults, Nutrients, 14, 4349, 10.3390/nu14204349 · DOI
  63. Lorenzoni, 2019, Food composition impacts the accuracy of wearable devices when estimating energy intake from energy-dense food, Nutrients, 11, 1170, 10.3390/nu11051170 · DOI
  64. Chou, 2024, An explanation for the accuracy of sensor-based measures of energy intake: amount of food consumed matters more than dietary composition, Appetite, 194, 107176, 10.1016/j.appet.2023.107176 · DOI
  65. Pettitt, 2016, A pilot study to determine whether using a lightweight, wearable micro-camera improves dietary assessment accuracy and offers information on macronutrients and eating rate, Br J Nutr, 115, 160, 10.1017/S0007114515004262 · DOI
  66. Chan, 2021, Using wearable cameras to assess foods and beverages omitted in 24 hour dietary recalls and a text entry food record app, Nutrients, 13, 1806, 10.3390/nu13061806 · DOI
  67. Scott, 2022, Feasibility of wearable camera use to improve the accuracy of dietary assessment among adults, J Nutr Sci, 11, e85, 10.1017/jns.2022.81 · DOI
  68. Ofei, 2019, Validation of a novel image-weighed technique for monitoring food intake and estimation of portion size in hospital settings: a pilot study, Public Health Nutr, 22, 1203, 10.1017/S1368980018001064 · DOI
  69. Papathanail, 2021, Evaluation of a novel artificial intelligence system to monitor and assess energy and macronutrient intake in hospitalised older patients, Nutrients, 13, 4539, 10.3390/nu13124539 · DOI
  70. Van Wymelbeke-Delannoy, 2022, A cross-sectional reproducibility study of a standard camera sensor using artificial intelligence to assess food items: the Foodintech project, Nutrients, 14, 221, 10.3390/nu14010221 · DOI
  71. Honda, 2022, A feasibility study for smartphone application to evaluate dietary realities after gastrointestinal surgery, Ann Cancer Res Therapy, 30, 33, 10.4993/acrt.30.33 · DOI
  72. Ryu, 2024, Sodium intake estimation in hospital patients using AI-based imaging: prospective pilot study, JMIR Format Res, 8, e48690, 10.2196/48690 · DOI
  73. Moyen, 2022, Relative validation of an artificial intelligence–enhanced, image-assisted mobile app for dietary assessment in adults: randomized crossover study, J Med Internet Res, 24, e40449, 10.2196/40449 · DOI
  74. Makhsous, 2020, Dietsensor: automatic dietary intake measurement using mobile 3D scanning sensor for diabetic patients, Sensors, 20, 3380, 10.3390/s20123380 · DOI
  75. Heikkilä, 2021, Agreement between an image-based dietary assessment method and a written food diary among adolescents with type 1 diabetes, Nutrients, 13, 1319, 10.3390/nu13041319 · DOI
  76. Rhyner, 2016, Carbohydrate estimation by a mobile phone-based system versus self-estimations of individuals with type 1 diabetes mellitus: a comparative study, J Med Internet Res, 18, e101, 10.2196/jmir.5567 · DOI
  77. Vasiloglou, 2018, A comparative study on carbohydrate estimation: GoCARB vs. dietitians, Nutrients, 10, 741, 10.3390/nu10060741 · DOI
  78. Kerr, 2017, BMI is associated with the willingness to record diet with a mobile food record among adults participating in dietary interventions, Nutrients, 9, 244, 10.3390/nu9030244 · DOI
  79. Borges, 2023, Accuracy of dietary intake assessments using food records based on photographic images captured by visually impaired people, PLoS One, 18, e0280725, 10.1371/journal.pone.0280725 · DOI
  80. Murphy, 2017, Measurements of daily energy intake and total energy expenditure in people with dementia in care homes: the use of wearable technology, J Nutr Health Aging, 21, 927, 10.1007/s12603-017-0870-y · DOI
  81. Turner-McGrievy, 2017, The dietary intervention to enhance tracking with mobile devices (DIET mobile) study: a 6-month randomized weight loss trial, Obesity, 25, 1336, 10.1002/oby.21889 · DOI
  82. Bell, 2022, Validity and feasibility of the monitoring and modeling family eating dynamics system to automatically detect in-field family eating behavior: observational study, JMIR Mhealth Uhealth, 10, e30211, 10.2196/30211 · DOI
  83. Saronga, 2022, A mixed-method study exploring experiences and perceptions of nutritionists regarding use of an image-based dietary assessment system in Tanzania, Nutrients, 14, 417, 10.3390/nu14030417 · DOI

Cited by

Metadata

Authors
Phalle, A., & Gokhale, D.
Venue
Frontiers in Nutrition
Publisher
Frontiers Media SA
Published
2025-01-23
Volume
12
ISSN
2296-861X
DOI
10.3389/fnut.2025.1518466
Cited by
70
References
4
Licence
creativecommons.org/licenses/by/4.0/ (vor)

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