Evidence map›Paper›PMID 42563987›Full record

ArticleFrontiers in nutrition2026

An AI-driven multivariate approach for personalized healthy eating recommendations aligned with sustainable healthy diet food-group guidelines.

Dimitris Tsolakidis, Vasilis Stamatis, Lazaros P Gymnopoulos, Vanda Pózner, Diána Szakal, Alba Lara Valtueña, Iris Blázquez, Patrick S Elliott, Lauren D Devine, Eileen R Gibney and 2 more

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Article in Frontiers in nutrition, 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

12 authors.

Dimitris TsolakidisThe Visual Computing Lab, Information Technologies Institute, Centre for Research and Technology Hellas (ITI-CERTH), Thessaloniki, Central Macedonia, Greece.
Vasilis StamatisThe Visual Computing Lab, Information Technologies Institute, Centre for Research and Technology Hellas (ITI-CERTH), Thessaloniki, Central Macedonia, Greece.
Lazaros P GymnopoulosThe Visual Computing Lab, Information Technologies Institute, Centre for Research and Technology Hellas (ITI-CERTH), Thessaloniki, Central Macedonia, Greece.
Vanda PóznerEnvironmental Social Science Research Group (ESSRG), Budapest, Hungary.
Diána SzakalEnvironmental Social Science Research Group (ESSRG), Budapest, Hungary.
Alba Lara ValtueñaFaculty of Health Sciences, Open University of Catalonia (Universitat Oberta de Catalunya, UOC), Barcelona, Spain.
Iris BlázquezFoodLab Research Group (2021SGR01357), Faculty of Health Sciences, Open University of Catalonia (Universitat Oberta de Catalunya, UOC), Barcelona, Spain.
Patrick S ElliottInstitute of Food and Health, School of Agriculture and Food Science, University College Dublin, Dublin, Ireland.
Lauren D DevineInstitute of Food and Health, School of Agriculture and Food Science, University College Dublin, Dublin, Ireland.
Eileen R GibneyInstitute of Food and Health, School of Agriculture and Food Science, University College Dublin, Dublin, Ireland.
Aifric M O'SullivanInstitute of Food and Health, School of Agriculture and Food Science, University College Dublin, Dublin, Ireland.
Kosmas DimitropoulosThe Visual Computing Lab, Information Technologies Institute, Centre for Research and Technology Hellas (ITI-CERTH), Thessaloniki, Central Macedonia, Greece.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Translating nutritional recommendations into practical day-to-day meal choices remains a challenging task, particularly when personalization, nutritional adequacy, dietary diversity, allergies, seasonal availability, and food-group constraints must be simultaneously satisfied. This study presents and evaluates the PLAN'EAT Nutrition Advisor, an Artificial Intelligence (AI)-driven, expert rule-based nutrition recommendation system, designed to generate personalized and nutritionally balanced weekly meal plans aligned with established dietary guidelines and food-group recommendations derived from Sustainable Healthy Diet (SHD) principles. The proposed approach is built upon the PLAN'EAT Expert-Curated Meal Database, a nutritionist-designed repository introduced in this work, comprising 401 expert-curated meals spanning Irish, Spanish, and Hungarian cuisines. Meal-plan generation follows a four-stage pipeline: (1)

Indexed as

artificial intelligencelarge language modelspersonalized nutritionrecommender systemsretrieval-augmented generationsupervised fine-tuningweekly meal planningweighted Euclidean distances

Identifiers

PMID42563987
PMCPMC13441720

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.