ArticleThe American journal of clinical nutrition2025
Customized multimodal Diabot-GPT-4o enhances accuracy of image-based dietary assessments in dietetic trainees in Taiwan: validation against weighed food records.
Article in The American journal of clinical nutrition, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundAutomated image-based dietary assessments (IBDAs) using multimodal artificial intelligence (AI) chatbots show strong potential. However, sources of error at the human-AI interface in real-world use remain unclear.
objectivesIn this study, we validated a GPT-4o-powered chatbot for automated IBDAs and identified key sources of error in free-living settings.
methodsIn total, 714 food images were collected from 3-d weighed food records (WFRs) across 171 d from 57 young adults. Images were analyzed using 4 AI configurations: Diabot (DB), DBFN (customized GPT-4o), 4o, and 4oFN (noncustomized), where "FN" indicates inclusion of the food name input. Portion sizes and nutrient estimates were compared with WFRs using Bland-Altman plots with equivalence testing at ±10%, ±15%, and ±20% bounds.
resultsUsing images alone, DB recognized 74% of food items versus 59% for 4o. All AI configurations provided accurate estimates of portion sizes (±10%-15%, coefficient of variation [CV]: 13%), energy (±10%-20%, CV: 14%), and carbohydrates (CHOs; ±15%-20%, CV: 15%) but showed less consistency for fats (±10%-22%, CV: 24%) and proteins (±10%->20.2%, CV: 18%). The custom DBFN outperformed 4oFN, achieving higher accuracy across more nutrients within the ±10% (weight, energy, fats, saturated fats, potassium, and magnesium), ±15% (proteins and sodium), and ±20% (CHOs and calcium) bounds and achieved the highest agreement with WFRs (Spearman's ρ = 0.863-0.662; Lin's concordance correlation coefficient = 0.874-0.540). Common errors at the human-AI interface included inaccurate portion-size estimates, obscured food visibility in images, poorly constructed prompts, omission or intrusion errors, and system-specific limitations, such as processing overload and configuration inconsistencies.
conclusionsCustomized AI chatbots improved automated IBDAs, yet accuracy depends on clear images for food visibility and portion-size fidelity. Standardized AI-input procedures (FN, cooking state, prompt structure, and configuration) and expert oversight to detect and correct AI hallucinations (fabricated items, units, or quantities) remain essential for reliable, interpretable estimates.
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