Academic Journal
Gluten-free diet plans generated by ChatGPT and Gemini differ in nutrient adequacy, while diet quality and carbon footprint are comparable.
| Τίτλος: | Gluten-free diet plans generated by ChatGPT and Gemini differ in nutrient adequacy, while diet quality and carbon footprint are comparable. |
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| Συγγραφείς: | Duman E; Department of Nutrition and Dietetics, Faculty of Health Sciences, Siirt University, Siirt, Türkiye. Electronic address: emre.duman@siirt.edu.tr., Songür Bozdağ AN; Department of Nutrition and Dietetics, Faculty of Health Sciences, İzmir Katip Çelebi University, İzmir, Türkiye., Memiç İnan C; Department of Nutrition and Dietetics, Faculty of Health Sciences, Hitit University, Çorum, Türkiye., Canbolat E; Department of Nutrition and Dietetics, Faculty of Health Sciences, Ankara University, Ankara, Türkiye. |
| Πηγή: | Nutrition (Burbank, Los Angeles County, Calif.) [Nutrition] 2026 Oct; Vol. 150, pp. 113323. Date of Electronic Publication: 2026 Jun 09. |
| Τύπος έκδοσης: | Journal Article; Comparative Study |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Elsevier Science Country of Publication: United States NLM ID: 8802712 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-1244 (Electronic) Linking ISSN: 08999007 NLM ISO Abbreviation: Nutrition Subsets: MEDLINE |
| Imprint Name(s): | Publication: Tarrytown, NY : Elsevier Science Original Publication: [Burbank, Calif. : Nutrition, c1987- |
| Ιατρικοί όροι (MeSH): | Carbon Footprint*/statistics & numerical data , Diet, Gluten-Free*/methods , Menu Planning*/methods, Micronutrients/analysis ; Humans ; Large Language Models ; Female ; Nutritive Value ; Adult ; Generative Artificial Intelligence |
| Περίληψη: | Background: Large language models are used for meal planning, but their suitability for restrictive diets remains unclear. We hypothesized that ChatGPT would generate gluten-free (GF) menus with lower energy and micronutrient adequacy than Gemini. Methods: In this exploratory prompt-based comparison, ChatGPT (GPT-4o) and Gemini (2.5 Flash) each generated six daily 2000 kcal GF menus for a hypothetical 30-year-old woman with celiac disease using a standardized prompt. Generation was repeated after 1 wk for test-retest and sensitivity analyses (12 menus/model). The nutrient composition was analyzed using BeBiS v9.0. Micronutrient adequacy was summarized using nutrient adequacy ratios (NARs; truncated at 1.00) and the mean adequacy ratio. Diet quality was assessed with the Diet Quality Index-International, and carbon footprint was estimated using sustainability assessment of food and diets factors. Between-model differences were assessed using t tests or exact Mann-Whitney U tests with multiplicity adjustment; short-term output variability was characterized using intraclass correlation coefficients and Bland-Altman plots. Results: In the initial dataset, ChatGPT menus provided less energy than Gemini menus (-379.8 kcal/d; P Conclusions: In this exploratory prompt-based study, ChatGPT and Gemini generated GF menus with different nutrient adequacy profiles under the tested conditions, whereas diet quality and carbon footprint were comparable. Given the limited prompt sample and observed output variability, these findings should be interpreted as hypothesis-generating. AI-generated GF menu outputs require standardized prompting, independent nutrient verification, GF safety assessment, and dietitian review before clinical use. (Copyright © 2026 Elsevier Inc. All rights reserved.) |
| Competing Interests: | Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. |
| Contributed Indexing: | Keywords: Carbon footprint; Diet quality; Gluten-free diet; Large language models; Nutrient adequacy |
| Substance Nomenclature: | 0 (Micronutrients) |
| Entry Date(s): | Date Created: 20260626 Date Completed: 20260805 Latest Revision: 20260805 |
| Update Code: | 20260806 |
| DOI: | 10.1016/j.nut.2026.113323 |
| PMID: | 42361723 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1873-1244 |
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| DOI: | 10.1016/j.nut.2026.113323 |