Toward Robust AI-Assisted Dietary Assessment for Diabetes Self-Management: Quantifying and Decomposing Large Language Model Prediction Variability From Meal Images
Unlabelled: Nutrient estimation from meal images by multimodal large language models can support diabetes self-management, but its robustness is limited by variability driven by both sensitivity to visual presentation and inherent model instability. Accounting for and mitigating this variability is essential for robust AI-assisted dietary assessment.
Authors
- Zhaohua Wang (ORCID: https://orcid.org/0000-0002-0209-5811)
- Kayo Waki (ORCID: https://orcid.org/0000-0003-0046-2523)
- Daniel Lane (ORCID: https://orcid.org/0009-0004-4638-465X)
Institutions
- University of Tokyo Hospital (JP)
- The University of Tokyo (JP)
Publication Details
- Journal
- JMIR Diabetes
- Published
- 2026-09-04
- DOI
- https://doi.org/10.2196/102715
- Primary Topic
- Nutritional Studies and Diet
- Type
- article
- Field-Weighted Citation Impact
- 0.00