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Data were collected from a 12-week randomized controlled trial (RCT: NCT04259632), in which participants recorded their meals in free-text format using the myCircadianClock app. Only de-identified data were used. We developed nutrition-focused retrieval-augmented generation (NutriRAG), an NLP framework that uses a retrieval-augmented generation approach to enhance food classification from free-text inputs. The framework retrieves relevant examples from a curated database and then leverages large language models, such as GPT-4, to classify user-recorded food items into predefined categories without fine-tuning. NutriRAG was then applied to data from the RCT, which included 77 adults with obesity recruited from the Twin Cities metro area and randomized into 3 intervention groups: time-restricted eating (TRE, 8-hs eating window), caloric restriction (CR, 15% reduction), and unrestricted eating.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>NutriRAG significantly enhanced classification accuracy and helped to analyze dietary habits, as noted by the retrieval-augmented GPT-4 model achieving a micro-F1 score of 82.24. Both interventions showed dietary alterations: CR participants ate fewer snacks and sugary foods, while TRE participants reduced nighttime eating.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>By using artificial intelligence, NutriRAG marks a substantial advancement in food classification and dietary analysis of nutritional assessments. The findings highlight NLP\u2019s potential to personalize nutrition and manage diet-related health issues, suggesting further research to expand these models for wider use.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamia\/ocag003","type":"journal-article","created":{"date-parts":[[2026,1,9]],"date-time":"2026-01-09T12:54:14Z","timestamp":1767963254000},"page":"802-811","source":"Crossref","is-referenced-by-count":5,"title":["NutriRAG: unleashing the power of large language models for food identification and classification through retrieval methods"],"prefix":"10.1093","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6524-5506","authenticated-orcid":false,"given":"Huixue","family":"Zhou","sequence":"first","affiliation":[{"name":"Institute for Health Informatics, University of Minnesota , Minneapolis, MN 55455,","place":["United 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