Work

Toward Robust AI-Assisted Dietary Assessment for Diabetes Self-Management: Quantifying and Decomposing Large Language Model Prediction Variability From Meal Images

Year: 2026

Type: article

Abstract: 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.

Source: JMIR Diabetes

DOI: 10.2196/102715

Authors: Zhaohua Wang, Daniel Lane, Kayo Waki

Institutions: The University of Tokyo, University of Tokyo Hospital

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDGs: Good health and well-being, Zero hunger

Open Access status: gold

Estimated APC paid: $1,873