{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T22:31:06Z","timestamp":1793745066451,"version":"4.6.1"},"reference-count":43,"publisher":"Cambridge University Press (CUP)","issue":"9","license":[{"start":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T00:00:00Z","timestamp":1744243200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["Br J Nutr"],"published-print":{"date-parts":[[2025,5,14]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    One of the most significant challenges in research related to nutritional epidemiology is the achievement of high accuracy and validity of dietary data to establish an adequate link between dietary exposure and health outcomes. Recently, the emergence of artificial intelligence (AI) in various fields has filled this gap with advanced statistical models and techniques for nutrient and food analysis. We aimed to systematically review available evidence regarding the validity and accuracy of AI-based dietary intake assessment methods (AI-DIA). In accordance with PRISMA guidelines, an exhaustive search of the EMBASE, PubMed, Scopus and Web of Science databases was conducted to identify relevant publications from their inception to 1 December 2024. Thirteen studies that met the inclusion criteria were included in this analysis. Of the studies identified, 61\u00b75 % were conducted in preclinical settings. Likewise, 46\u00b72 % used AI techniques based on deep learning and 15\u00b73 % on machine learning. Correlation coefficients of over 0\u00b77 were reported in six articles concerning the estimation of calories between the AI and traditional assessment methods. Similarly, six studies obtained a correlation above 0\u00b77 for macronutrients. In the case of micronutrients, four studies achieved the correlation mentioned above. A moderate risk of bias was observed in 61\u00b75 % (\n                    <jats:italic>n<\/jats:italic>\n                    8) of the articles analysed, with confounding bias being the most frequently observed. AI-DIA methods are promising, reliable and valid alternatives for nutrient and food estimations. However, more research comparing different populations is needed, as well as larger sample sizes, to ensure the validity of the experimental designs.\n                  <\/jats:p>","DOI":"10.1017\/s0007114525000522","type":"journal-article","created":{"date-parts":[[2025,4,10]],"date-time":"2025-04-10T05:39:39Z","timestamp":1744263579000},"page":"1241-1253","update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":33,"title":["Validity and accuracy of artificial intelligence-based dietary intake assessment methods: a systematic review"],"prefix":"10.1017","volume":"133","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4899-7941","authenticated-orcid":false,"given":"Sebasti\u00e1n","family":"Cofre","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Camila","family":"Sanchez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gladys","family":"Quezada-Figueroa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xaviera A.","family":"L\u00f3pez-Cort\u00e9s","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2025,4,10]]},"reference":[{"key":"S0007114525000522_ref41","first-page":"52","article-title":"Guidance of development, validation, and evaluation of algorithms for populating health status in observational studies of routinely collected data (DEVELOP-RCD)","volume":"11","author":"Wang","year":"2024","journal-title":"Mil Med Res"},{"key":"S0007114525000522_ref14","doi-asserted-by":"publisher","DOI":"10.1139\/apnm-2021-0448"},{"key":"S0007114525000522_ref40","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclinepi.2020.09.045"},{"key":"S0007114525000522_ref39","doi-asserted-by":"publisher","DOI":"10.2499\/p15738coll2.134412"},{"key":"S0007114525000522_ref19","doi-asserted-by":"publisher","DOI":"10.3390\/nu14030537"},{"key":"S0007114525000522_ref10","doi-asserted-by":"publisher","DOI":"10.1007\/s40137-021-00297-3"},{"key":"S0007114525000522_ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclinepi.2018.08.003"},{"key":"S0007114525000522_ref33","first-page":"1","article-title":"Deep neural networks for image-based dietary assessment","volume":"169","author":"Mezgec","year":"2021","journal-title":"J Vis 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assessment methods: dietary records","volume":"31","author":"Ortega","year":"2015","journal-title":"Nutr Hosp"},{"key":"S0007114525000522_ref2","doi-asserted-by":"publisher","DOI":"10.1007\/s00394-021-02545-9"},{"key":"S0007114525000522_ref28","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-21421-y"},{"key":"S0007114525000522_ref13","doi-asserted-by":"publisher","DOI":"10.1080\/10408398.2021.1956425"},{"key":"S0007114525000522_ref38","doi-asserted-by":"publisher","DOI":"10.1093\/cdn\/nzac077.037"}],"container-title":["British Journal of 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Author(s), 2025. Published by Cambridge University Press on behalf of The Nutrition Society","name":"copyright","label":"Copyright","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}},{"value":"This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https:\/\/creativecommons.org\/licenses\/by\/4.0\/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.","name":"license","label":"License","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}