ArticleCurrent developments in nutrition2025
Performance Evaluation of 3 Large Language Models for Nutritional Content Estimation from Food Images.
Article in Current developments in nutrition, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Generative AI in Precision Nutrition: A Review of Current Developments and Future Directions.Nutrients · 2026Pooled it
- Article
- Toward Robust AI-Assisted Dietary Assessment for Diabetes Self-Management: Quantifying and Decomposing Large Language Model Prediction Variability From Meal Images.JMIR diabetes · 2026Article
- Article
- Event-Driven Multimodal Sensing and Computing for Context-Aware Home Monitoring Using Stereo Vision and Dietary Event Anchoring.Sensors (Basel, Switzerland) · 2026Article
- Prompt Engineering and Model Selection for LLM-Based Nutritional Estimation from Food Images: A Multi-Dataset Investigation.Nutrients · 2026Article
- Text-based nutrient estimation by large language models: a comparative evaluation against the Chinese Food Composition Table.Frontiers in nutrition · 2026Article
- Large Language Models for Real-World Nutrition Assessment: Structured Prompts, Multi-Model Validation and Expert Oversight.Nutrients · 2025Article
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4 authors.
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Abstract
Background: Traditional dietary assessment methods face limitations including recall bias, participant burden, and portion size estimation errors. Recent advances in artificial intelligence, particularly multimodal large language models (LLMs), offer potential solutions for automated nutritional analysis from food images. Objectives: This study aims to evaluate and compare the performance of 3 leading LLMs (ChatGPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro) in estimating food weight, energy content, and macronutrient composition from standardized food photographs. Methods: We analyzed 52 standardized food photographs including individual food components ( Results: ChatGPT and Claude demonstrated similar accuracy with MAPE values of 36.3% and 37.3% for weight estimation, and 35.8% for energy estimation. Gemini showed substantially higher errors across all nutrients (MAPE 64.2%-109.9%). Correlations between model estimates and reference values ranged from 0.65 to 0.81 for ChatGPT and Claude, compared with 0.58-0.73 for Gemini. All models exhibited systematic underestimation that increased with portion size, with bias slopes ranging from -0.23 to -0.50. Conclusions: ChatGPT and Claude achieved accuracy levels comparable with traditional self-reported dietary assessment methods but without associated user burden, suggesting potential utility as dietary monitoring tools. However, systematic underestimation of large portions and high variability in macronutrient estimation indicate these general-purpose LLMs are not yet suitable for precise dietary assessment in clinical or athletic populations where accurate quantification is critical.
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