Assessing AI-based chatbots accuracy in caloric estimation: A focus on traditional Turkish foods - Kaya Kaçar, Hüsna; Sarıkaya, Buse (The European Research Journal, 2025)
Objectives: To evaluate the accuracy of three widely used AI-based chatbots - ChatGPT 4.0, Microsoft Copilot, and Gemini - in estimating the caloric values of traditional Turkish foods. Methods: The accuracy of caloric information provided by the chatbots for 71 traditional Turkish foods selected from the Türkomp National Food Composition Database was assessed. Each chatbot was queried in Turkish using the standardised prompt: "What is the caloric content of [food name] per 100 grams?" Responses were recorded and, when necessary, converted to a per-100-gram basis to ensure consistency. Accuracy percentages were calculated by comparing chatbot responses to Türkomp reference values, and extreme deviations were adjusted to 0%. Mean accuracy scores and distribution across predefined accuracy intervals were analysed for each chatbot. Statistical analysis was conducted to determine the mean accuracy percentages and identify differences among the chatbots. Results: ChatGPT 4.0 achieved the highest mean accuracy (81.62%±20.6%), followed closely by Microsoft Copilot (81.23%±20.7%), while Gemini demonstrated lower accuracy (70.99%±30.2) (P