Evidence map›Paper›PMID 41845048›Full record

ArticleNPJ digital medicine2026

Limited validity of an AI-powered app for dietary assessment in females with obesity.

Michele Serra, Daniela Alceste, Nicole Jucker, Lotta Haupt, Sebastian Elben, Samuel Müller, Paul J M Hulshof, Harro A J Meijer, Andreas Thalheimer, Robert E Steinert and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
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

14 authors.

Michele Serra *Department of Endocrinology, Diabetology and Clinical Nutrition, University Hospital Zurich, Zurich, Switzerland.
Daniela Alceste *Department of Visceral and Transplant Surgery, University Hospital Zurich, Zurich, Switzerland.
Nicole JuckerDepartment of Endocrinology, Diabetology and Clinical Nutrition, University Hospital Zurich, Zurich, Switzerland.
Lotta HauptDepartment of Health Sciences and Technology, ETH Zürich, Zurich, Switzerland.
Sebastian ElbenDepartment of Health Sciences and Technology, ETH Zürich, Zurich, Switzerland.
Samuel MüllerDepartment of Health Sciences and Technology, ETH Zürich, Zurich, Switzerland.
Paul J M HulshofDivision of Human Nutrition, Wageningen University, Wageningen, The Netherlands.
Harro A J MeijerCentre for Isotope Research (CIO), Energy and Sustainability Research Institute Groningen, University of Groningen, Groningen, The Netherlands.
Andreas ThalheimerDepartment of General Surgery, Hospital Männedorf, Männedorf, Switzerland.
Robert E SteinertDepartment of Visceral and Transplant Surgery, University Hospital Zurich, Zurich, Switzerland.
Philipp A GerberDepartment of Endocrinology, Diabetology and Clinical Nutrition, University Hospital Zurich, Zurich, Switzerland.
Alan C SpectorDepartment of Psychology and Program in Neuroscience, Florida State University, Tallahassee, FL, USA.
Daniel GeroDepartment of Visceral and Transplant Surgery, University Hospital Zurich, Zurich, Switzerland.
Marco BueterDepartment of General Surgery, Hospital Männedorf, Männedorf, Switzerland. marco.bueter@uzh.ch.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is transforming dietary assessment, yet few tools have been clinically validated against physiological reference methods. This cross-sectional observational validation study conducted under free-living conditions evaluated the validity of SNAQ, an AI-powered image-based dietary assessment app, against doubly labelled water (DLW) in females with obesity. Twenty participants completed a 7-day protocol, including DLW-based measurement of total daily energy expenditure (TDEE) and estimation of total daily energy intake using SNAQ and 24-h dietary recall (24HR). Compared with DLW-derived TDEE (3004 ± 481 kcal/day), SNAQ underestimated energy intake by 25% (bias -817 kcal/day; limits of agreement -3707 to 2073 kcal/day), while 24HR underestimated intake by 50%. Individual-level agreement had negligible within-subject reliability (ICC = 0.00). Despite advanced AI architecture, SNAQ showed systematic group-level underestimation and poor individual-level agreement, underscoring the translational gap between algorithmic performance and clinical feasibility and the need for standardised clinical validation before implementation.

Identifiers

PMID41845048
PMCPMC13144502

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Registered trials

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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.