{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T17:38:27Z","timestamp":1792777107357,"version":"4.5.0"},"reference-count":53,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T00:00:00Z","timestamp":1792540800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Nutr Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Artificial intelligence (AI) tools are increasingly being integrated into nutrition care and dietary counseling. Evaluating their usefulness, accuracy, and limitations is essential before widespread clinical adoption.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>To systematically review the evidence on the performance of ChatGPT and related AI tools in nutrition and dietetics, including comparisons with other AI models and human experts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This systematic review followed PRISMA guidelines and included studies published until February 2025. Searches were conducted in PubMed, Scopus, Cochrane Library, and Google Scholar using keywords related to ChatGPT, AI, nutrition, and diet. Of 5504 retrieved records, 31 studies met the inclusion criteria. Data were synthesized under three domains: ChatGPT's performance in nutritional guidance, comparisons with other AI tools, and comparisons with human experts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Summary<\/jats:title>\n                    <jats:p>Most included studies were cross-sectional and published in 2024, with ChatGPT-3.5 and 4.0 being the most evaluated models. ChatGPT showed high-accuracy in clinical guideline adherence for conditions such as cardiovascular disease and diabetes, moderate performance in dietetics examination simulations, and variable accuracy in disease-specific diet planning. It performed well in nutrition education, dietary recall, and food recognition, but demonstrated limitations in micronutrient estimation, renal nutrition safety, and reproducibility. Compared with other AI tools, ChatGPT showed stronger clinical reasoning but lower performance in specialized food-classification tasks. Compared with human experts, it matched performance in general nutrition knowledge but lacked cultural competence and emotional intelligence. AI tools may complement nutrition care, but human dietitians remain essential for personalized care.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/02601060261499594","type":"journal-article","created":{"date-parts":[[2026,10,21]],"date-time":"2026-10-21T13:09:31Z","timestamp":1792588171000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Digital dietitians? Assessing ChatGPT's role in transforming nutrition care and dietetic practice: A systematic review"],"prefix":"10.1177","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-5103-0877","authenticated-orcid":false,"given":"Shivani","family":"Phugat","sequence":"first","affiliation":[{"name":"Institute of Liver and Biliary Sciences","place":["India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Diksha","family":"Agrawat","sequence":"additional","affiliation":[{"name":"Lady Irwin College","place":["India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anjani","family":"Bakshi","sequence":"additional","affiliation":[{"name":"Lady Irwin College","place":["India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4383-000X","authenticated-orcid":false,"given":"Neha","family":"Bakshi","sequence":"additional","affiliation":[{"name":"Lady Irwin College","place":["India"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,10,21]]},"reference":[{"issue":"8","key":"e_1_3_3_2_1","first-page":"185","article-title":"AI-driven patient education in chronic kidney disease: Evaluating chatbot responses against clinical guidelines","volume":"12","author":"Acharya PC","year":"2024","unstructured":"Acharya PC, Alba R, Krisanapan P, et al. (2024) AI-driven patient education in chronic kidney disease: Evaluating chatbot responses against clinical guidelines. 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