{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T06:03:56Z","timestamp":1789365836628,"version":"build-2803163510"},"reference-count":59,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T00:00:00Z","timestamp":1789344000000},"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. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Diet is one of the key factors influencing health. Adherence to the principles of a healthy diet determines whether many diseases occur or are prevented. Numerous guidelines and recommendations have been issued to facilitate the adoption of healthy eating practices. However, putting them into practice can be problematic. In recent years, increasing attention has been paid to large language models (LLMs). A growing number of reports also point to the risks arising from the uncritical adoption by the general public of health recommendations generated by LLMs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>The aim of this study was to evaluate whether meal plans generated by LLMs comply with Polish healthy eating recommendations under real-world average-user conditions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and methods<\/jats:title>\n                    <jats:p>The three widely used LLMs \u2013 ChatGPT, Gemini and Copilot \u2013 were used to generate diets. Each model generated 35\u202fdays of meal plans across three specified energy levels: 2,000\u202fkcal, 2,500\u202fkcal and 3,000\u202fkcal (in total, each model generated 105\u202fdays of meal plans). The menus were assessed for compliance with the recommendations based on the average number of guidelines met each day and the total number of guidelines met.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Copilot achieved the highest overall compliance score (median 7\/9 recommendations per day), significantly outperforming ChatGPT (median 6\/9;\n                      <jats:italic>p<\/jats:italic>\n                      \u202f=\u202f0.002) and Gemini (median 5\/9;\n                      <jats:italic>p<\/jats:italic>\n                      \u202f&amp;lt;\u202f0.001). Compliance was generally independent of the target energy level. All models consistently limited red and processed meat intake and usually met recommendations regarding fruit and vegetable consumption, sweets, fats, and dairy products. However, adherence was poor for wholegrain products. None of the evaluated models achieved full compliance with all tested national dietary recommendations.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Diets generated by LLMs demonstrated moderate compliance with Polish dietary recommendations, although important shortcomings remained. While AI-generated meal plans may encourage healthier dietary patterns in some areas, they cannot yet be considered fully consistent with national dietary guidance. LLM-generated meal plans should be interpreted with caution and should not replace professional dietary counseling.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1949700","type":"journal-article","created":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T05:38:35Z","timestamp":1789364315000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Compliance of large language model generated meal plans with polish dietary recommendations: a real-world evaluation under average user conditions\u20143(LM)Diet study"],"prefix":"10.3389","volume":"14","author":[{"given":"Hubert","family":"Dobrowolski","sequence":"first","affiliation":[{"name":"School of Medical & Health Sciences, VIZJA University","place":["Warsaw, 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