Shivani Phugat, Prabudh Goel
The traditional paper-based approaches to dietary or nutrient tracking are being replaced by advanced mobile applications providing immediate, individualized nutritional feedback. This is due to the quick shift in consumer preferences toward smartphone-based health management, and the urgent need for varied, culturally sensitive nutritional analysis systems is met by flexibility in food detection without fine-tuning, effectively switching from generic food recognition to dishes that are specific to a given culture when given prompts about local cuisine provided by the smartphone-based applications. AI-powered food recognition technology has been successfully incorporated into consumer apps like MyFitnessPal, HealthifyMe, and Foodvisor, showing higher user retention rates and dietary adherence than manual entry systems. Patients requiring personalized recommendations are served by specialized healthcare applications that use automated image analysis and simplified user interfaces. Food variability across preparation methods and cultural contexts, mixed dish recognition complexity, and portion size estimation accuracy (65–75%) remain technical challenges despite significant advancements. To address this global food diversity and enhance model generalization across various cuisines and preparation variations, focus should be on creating larger, more varied datasets, advanced augmentation techniques, and synthetic data generation methods.