Carbohydrate counting in traditional Turkish fast foods for individuals with type 1 diabetes: Can artificial intelligence models replace dietitians?

OBJECTIVES: Carbohydrate counting is a recommended approach for achieving glycemic control in individuals with type 1 diabetes (T1D). This study aimed to compare the accuracy of carbohydrate content estimations for traditional Turkish fast foods made by artificial intelligence (AI) models and dietitian. METHODS: Children and adolescents with T1D were pretested to identify the 12 most preferred Turkish fast-food items. Standardized recipes were developed for these meals, and the meals were photographed under standardized angular and lighting conditions. The photos were then uploaded to AI applications (ChatGPT-4.0, DeepSeek, Gemini, and CarbManager) and each model was prompted to estimate the carbohydrate content of the respective food items. Dietitians were asked to estimate the carbohydrate content based on these photographs. RESULTS: Of the dietitians in the study (n = 40), 50% had postgraduate education, and 17.5% of those providing carbohydrate counting education (n = 20, 50.0%) had been doing so for more than 7 y. No significant difference was found between the carbohydrate estimates of dietitians who provided and those who did not provide carbohydrate counting training (P > 0.05). The intraclass correlation coefficient (ICC) between the AI models was 0.3554 (95% confidence interval [CI]: 0.0974-0.6801), indicating low reliability. The highest agreement with the estimates of dietitians who provided carbohydrate counting training (ICC = 0.417, 95% CI: 0.247-0.685) and those who did not (ICC = 0.307, 95% CI: 0.163-0.578) was observed with ChatGPT. CONCLUSIONS: AI models can assist individuals with diabetes and healthcare professionals in estimating the carbohydrate content of foods, and consequently, can make a significant contribution to diabetes self-management.