AI-generated menus versus the Türkiye Dietary Guidelines: nutrient adequacy, ultra-processed food burden, cost, and carbon footprint

Introduction While the use of generative AI tools in meal planning is becoming increasingly widespread, the alignment of the menus they generate with national dietary guidelines, the level of food processing, economic accessibility, and environmental impacts have not yet been sufficiently clarified. This study aimed to compare daily menus generated by ChatGPT and Gemini for different life stages and physiological conditions with the Türkiye Dietary Guidelines (TÜBER) in terms of nutrient adequacy, ultra-processed food (UPFs) content, economic cost, carbon footprint, and short-term temporal consistency. Methods Using ChatGPT-4o and Gemini Pro (Google), three menus were generated at 2-week intervals for eight population profiles: preschool children, school-age children, adolescents, adult women, adult men, older adults, pregnant women, and lactating women. A total of 48 AI-generated menus (24 from ChatGPT and 24 from Gemini) were compared with 8 profile-specific reference menus included in TÜBER. Energy and nutrient content were analyzed using BeBiS 9; NOVA 4 content, daily cost, and carbon footprint were calculated. Results Energy adequacy was 95.2%, 95.1%, and 93.4% in the ChatGPT, Gemini, and TÜBER menus, respectively. A significant difference in carbohydrate adequacy was observed among the menu sources (Holm-adjusted omnibus p = 0.018), with the difference specifically identified between the Gemini and TÜBER menus (Holm-adjusted pairwise p = 0.024, r = 0.89). Dietary fiber adequacy differed significantly among menu sources (Holm-adjusted omnibus p = 0.040), with post-hoc analysis showing higher adequacy in ChatGPT than in Gemini menus (Holm-adjusted pairwise p = 0.024, r = 0.89). The proportion of menus containing at least one NOVA 4 component was 16.7% for ChatGPT and 41.7% for Gemini. The median daily costs for ChatGPT, Gemini, and TÜBER menus were 485.6, 449.8, and 405.2 ₺, respectively; the carbon footprints were 6.4, 7.3, and 6.7 kg CO 2 -eq/day, respectively, and the analyses provided insufficient evidence of differences among menu sources for either cost or carbon footprint. Discussion In the analysis of daily menus, although generative AI models generally produced energy values close to profile-specific references, limitations were observed in terms of nutritional balance, UPFs content, and output consistency. In nutrition planning, the dietitian must be the primary decision-maker, and AI outputs must be validated and individualized in accordance with evidence-based national guidelines such as TÜBER.