{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T23:36:51Z","timestamp":1790638611312,"version":"4.1.0"},"reference-count":64,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T00:00:00Z","timestamp":1784332800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Nutrients"],"abstract":"<jats:p>Background\/Objectives: AI tools are becoming increasingly common, including in areas related to health and diet. However, there are no studies indicating the nutritional value of diets generated by large language models (LLMs). The aim of this study was to analyse the nutritional value of diets generated by three major language models. Methods: Three LLMs were used in the study: ChatGPT, Gemini and Copilot. Using each of these, 35-day meal plans were generated for three energy variants: 2000 kcal, 2500 kcal and 3000 kcal. The meal plans were then analysed using dietary software. Results: All LLMs generated meal plans with an insufficient energy value (p &lt; 0.001, one-sample t-test) by 284\u2013546 kcal, depending on the model and the targeted energy content. The meal plans were characterised by an excessively high proportion of energy from protein and an excessively low proportion of energy from fat. Carbohydrates were planned in the correct amounts. ChatGPT and Copilot generated meal plans with a generally adequate vitamin and mineral content. Gemini generated meal plans that were often deficient, particularly for diets with lower energy values. Conclusions: Diets generated by LLMs have many shortcomings. Unsupervised use of LLM-generated meal plans by non-expert users may produce nutritionally inaccurate diets and should not replace professional dietary counselling.<\/jats:p>","DOI":"10.3390\/nu18142363","type":"journal-article","created":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T02:32:08Z","timestamp":1784514728000},"page":"2363","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An Analysis of the Nutritional Value of Diets Generated by Large Language Models Under Average User Conditions\u20143(LM)Diet Study"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7445-8036","authenticated-orcid":false,"given":"Hubert","family":"Dobrowolski","sequence":"first","affiliation":[{"name":"School of Medical & Health Sciences, VIZJA University, Okopowa 59 Str., 01-043 Warsaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1625","DOI":"10.1016\/S0140-6736(25)01201-2","article-title":"The EAT\u2013Lancet Commission on Healthy, Sustainable, and Just Food Systems","volume":"406","author":"Thilsted","year":"2025","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"102848","DOI":"10.1016\/j.eclinm.2024.102848","article-title":"Burden of Disease Attributable to High Body Mass Index: An Analysis of Data from the Global Burden of Disease Study 2021","volume":"76","author":"Zhou","year":"2024","journal-title":"eClinicalMedicine"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1813","DOI":"10.1377\/hlthaff.2015.0606","article-title":"The Complex Relationship between Diet and Health","volume":"34","author":"Bleich","year":"2015","journal-title":"Health Aff."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Yang, W., Jiang, W., and Guo, S. 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