Evaluating a Multitask AI Model versus Humans for Portion Size Estimation

Background: Accurate dietary assessment is essential for precision nutrition and effective nutrition surveillance. However, portion size estimation remains a persistent challenge, particularly in culturally diverse regions such as Central Asia. Traditional self-reporting tools often yield inconsistent results due to communal eating practices and unfamiliarity with standard measures. Objective: To address these limitations, this study aimed to compare three methods: unassisted human judgment, visual food atlas assistance, and an artificial intelligence (AI) model, using Central Asian food items. Methods: In this cross-sectional study, 128 participants from Astana, Kazakhstan, visually estimated portion sizes of 51 foods and 8 beverages from standardized photographs. Participants were randomized into two groups: one using unassisted visual estimation and the other aided by a regionally tailored digital food atlas. Additionally, an AI model trained on Central Asian food images was evaluated. Actual food weights served as the reference standard. Accuracy was assessed using Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) across food types and portion sizes. Results: The atlas-assisted group demonstrated the highest accuracy, with the lowest MAE (80.81g) and MAPE (44.76%) across all portions. The AI model showed promising results for average portions (MAE: 79.07g, MAPE: 67.91%) but underperformed on small portions, particularly for meat-based items. Unassisted estimates were the least accurate (MAE: 133.86g, MAPE: 79.40%). Across food categories, visual aids consistently improved accuracy, while AI demonstrated variability by texture and portion size. Conclusions: Culturally adapted visual atlases significantly enhance portion size estimation accuracy in non-Western, communal-eating contexts. While AI models hold promise for dietary assessments, particularly with standard portions and beverages, further refinement is needed for complex food items and small portion types. These findings support the integration of visual and AI-based tools into region-specific dietary monitoring strategies.