{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T12:45:48Z","timestamp":1793191548017,"version":"4.5.0"},"reference-count":88,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T00:00:00Z","timestamp":1737590400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Nutr."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>A total of 17,613 original, full-text articles using keywords such as \u201cartificial intelligence OR food image analysis OR wearable devices AND dietary OR nutritional assessment,\u201d 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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 \u201cImage-based\u201d and \u201cMotion sensor-based.\u201d 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 &amp;amp; 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnut.2025.1518466","type":"journal-article","created":{"date-parts":[[2025,1,23]],"date-time":"2025-01-23T00:10:56Z","timestamp":1737591056000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":68,"title":["Navigating next-gen nutrition care using artificial intelligence-assisted dietary assessment tools\u2014a scoping review of potential applications"],"prefix":"10.3389","volume":"12","author":[{"given":"Anuja","family":"Phalle","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Devaki","family":"Gokhale","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,1,23]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"1359","DOI":"10.3390\/nu14071359","article-title":"Telenutrition: changes in professional practice and in the nutritional assessments of Italian dietitian nutritionists in the COVID-19 era","volume":"14","author":"Gnagnarella","year":"2022","journal-title":"Nutrients"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"nzaa124","DOI":"10.1093\/cdn\/nzaa124","article-title":"COVID-19 and telenutrition: remote consultation in clinical nutrition practice","volume":"4","author":"Farid","year":"2020","journal-title":"Curr Dev Nutr"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"2288","DOI":"10.1093\/nutrit\/nuac033","article-title":"Artificial intelligence in food science and nutrition: a narrative review","volume":"80","author":"Miyazawa","year":"2022","journal-title":"Nutr Rev"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.3390\/nu16071073","article-title":"Applications of artificial intelligence, machine learning, and deep learning in nutrition: a systematic review","volume":"16","author":"Theodore Armand","year":"2024","journal-title":"Nutrients"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"650","DOI":"10.1016\/j.jand.2021.07.004","article-title":"Nutrition care process quality evaluation and standardization tool: the next frontier in quality evaluation of documentation","volume":"122","author":"Lewis","year":"2022","journal-title":"J Acad Nutr Diet"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"404","DOI":"10.1017\/S1368980018002951","article-title":"Dietary assessment toolkits: an overview","volume":"22","author":"Dao","year":"2019","journal-title":"Public Health Nutr"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"e2022017","DOI":"10.23751\/pn.v24i1.11107","article-title":"Traditional dietary assessment tools verses technology-based methods in children and adolescence. 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