Ecological and nutritional analysis of artificial intelligence-based sustainable dietary models

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dc.authorid0000-0002-1383-9652
dc.contributor.authorKokturk, Seda Nur
dc.contributor.authorYardimci, Hulya
dc.date.accessioned2026-02-28T12:17:54Z
dc.date.available2026-02-28T12:17:54Z
dc.date.issued2026
dc.departmentBayburt Üniversitesi
dc.description.abstractGrowing concerns over environmental sustainability and health have increased the demand for sustainable diets. Advances in artificial intelligence (AI) offer new opportunities for diet planning. Evaluating the nutritional adequacy and environmental impact of AI-generated sustainable diets is essential. This study aimed to compare the nutritional quality and ecological footprints of sustainable diets created by four popular AI tools (Gemini, Copilot, ChatGPT, and Grok). Each AI tool generated a sustainable diet plan for a healthy adult woman. Diets were evaluated for energy, nutrient content, antioxidant capacity, glycemic index, protein quality, fatty acid profiles, Healthy Eating Index-2015 scores, and ecological footprints. All diets showed low ecological footprints but did not meet energy targets (1800 kcal/day). Gemini had the most sustainable composition. Vitamin B12 was consistently below recommended levels in diets from Gemini, Copilot, and ChatGPT. All diets had adequate carbohydrate, protein, fat quality, and overall diet quality. Carbon footprint associated with protein, energy from protein, cholesterol, pyridoxine, folate, and vitamin C. Water footprint was linked to multiple nutrients, and fruits, meat, poultry, and fish; vegetable intake correlated with carbon footprint. AI-assisted diets have potential for sustainability but nutritional completeness concerns remain, especially for vulnerable groups. Further studies comparing AI and dietitian plans are needed to assess long-term effects.
dc.identifier.doi10.1016/j.jfca.2025.108814
dc.identifier.issn0889-1575
dc.identifier.issn1096-0481
dc.identifier.scopus2-s2.0-105025236677
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1016/j.jfca.2025.108814
dc.identifier.urihttps://hdl.handle.net/20.500.12403/6002
dc.identifier.volume149
dc.identifier.wosWOS:001648527000001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherAcademic Press Inc Elsevier Science
dc.relation.ispartofJournal of Food Composition And Analysis
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260218
dc.subjectCarbon footprint
dc.subjectWater footprint
dc.subjectArtificial intelligence
dc.subjectSustainable diet
dc.titleEcological and nutritional analysis of artificial intelligence-based sustainable dietary models
dc.typeArticle

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