Evidence map›Paper›PMID 40087237›Full record

ReviewCurrent nutrition reports2025

Assessing the Links Between Artificial Intelligence and Precision Nutrition.

Danton Diego Ferreira, Lívia Garcia Ferreira, Katiúcia Alves Amorim, Deyvis Cabrini Teixeira Delfino, Ana Cláudia Barbosa Honório Ferreira, Leandra Passarelli Castro E Souza

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current nutrition reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Review
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  9. From Food to Clinic: Mapping FoodOn to the UMLS to Enable Nutritional Decision Support.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024
    Article
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

6 authors.

Danton Diego FerreiraDepartment of Automatic, Universidade Federal de Lavras, Lavras, Minas Gerais, Brazil. danton@ufla.br.ORCID http://orcid.org/0000-0002-4504-7721
Lívia Garcia FerreiraNutrition and Health Graduate Program, Universidade Federal de Lavras, Lavras, Minas Gerais, Brazil.ORCID http://orcid.org/0000-0002-8089-351X
Katiúcia Alves AmorimDepartment of Food Science, Universidade Federal de Lavras, Lavras, Minas Gerais, Brazil.ORCID http://orcid.org/0000-0001-5205-8114
Deyvis Cabrini Teixeira DelfinoSignal Processing and Telecommunications, Universidade Federal de Juiz de Fora, Juiz de Fora, Minas Gerais, Brazil.ORCID http://orcid.org/0009-0003-6052-4746
Ana Cláudia Barbosa Honório FerreiraCentro Universitário de Lavras - UNILAVRAS, Lavras, Minas Gerais, Brazil.ORCID http://orcid.org/0000-0003-3661-5632
Leandra Passarelli Castro E SouzaDepartment of Nutrition, Universidade Federal de Lavras, Lavras, Minas Gerais, Brazil.ORCID http://orcid.org/0009-0000-4828-2789

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewTo conduct an overview of the potentialities of artificial intelligence in precision nutrition. RECENT

findingsA keyword co-occurrence analysis of 654 studies on artificial intelligence (AI) and precision nutrition (PN) highlighted the potential of AI techniques like Random Forest and Gradient Boosting in improving personalized dietary recommendations. These methods address gastrointestinal symptoms, weight management, and cardiometabolic markers, especially when incorporating data on gut microbiota. Despite its promise, challenges like data privacy, bias, and ethical concerns remain. AI must complement healthcare professionals, necessitating clear guidelines, robust governance, and ongoing research to ensure safe and effective applications. The integration of AI into PN enables highly personalized dietary recommendations by accounting for metabolic variability, genetics, and microbiome data. AI-driven strategies show potential in managing conditions like obesity and diabetes through accurate predictions of individual dietary responses. However, ethical, regulatory, and practical challenges must be addressed to ensure safe, equitable, and effective application of AI in nutrition.

Indexed as

Artificial IntelligencePrecision MedicineGastrointestinal MicrobiomeHumansArtificial intelligenceDeep learningMachine learningMicrobiomePersonalized nutritionPrecision nutrition

Identifiers

PMID40087237

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.