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

Abstract read

In one paragraph

Article in JMIR diabetes, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Zhaohua WangDepartment of Health and Behavioral Informatics & Therapeutics (HABIT), Graduate School of Medicine, The University of Tokyo, Faculty of Medicine Bldg. 2, 2nd Floor, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan, 81 3-5841-1892 ext 21892.ORCID http://orcid.org/0009-0008-1778-1196

Daniel LaneDepartment of Health and Behavioral Informatics & Therapeutics (HABIT), Graduate School of Medicine, The University of Tokyo, Faculty of Medicine Bldg. 2, 2nd Floor, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan, 81 3-5841-1892 ext 21892.ORCID http://orcid.org/0009-0004-4638-465X

Kayo WakiDepartment of Health and Behavioral Informatics & Therapeutics (HABIT), Graduate School of Medicine, The University of Tokyo, Faculty of Medicine Bldg. 2, 2nd Floor, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan, 81 3-5841-1892 ext 21892.ORCID http://orcid.org/0000-0003-0046-2523

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

artificial intelligencedietary assessmentmultimodal large language modelsnutrient estimationprediction variability

Identifiers

PMID42696510

DOI10.2196/102715

PMCPMC13544413

What OpenQuestion holds

Textmetadata

Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.