Evidence map›Paper›PMID 38971229›Full record

SynthesisAdvances in nutrition (Bethesda, Md.)2024

Artificial Intelligence in Malnutrition: A Systematic Literature Review.

Sander Mw Janssen, Yamine Bouzembrak, Bedir Tekinerdogan

Abstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Advances in nutrition (Bethesda, Md.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 4 pooled it
–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

15 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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  4. Machine Learning in Predicting Child Malnutrition: A Meta-Analysis of Demographic and Health Surveys Data.International journal of environmental research and public health · 2025
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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.

Sander Mw JanssenInformation Technology Group, Wageningen University and Research, Wageningen, The Netherlands.
Yamine BouzembrakInformation Technology Group, Wageningen University and Research, Wageningen, The Netherlands. Electronic address: yamine.bouzembrak@wur.nl.
Bedir TekinerdoganInformation Technology Group, Wageningen University and Research, Wageningen, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Malnutrition among the population of the world is a frequent yet underdiagnosed problem in both children and adults. Development of malnutrition screening and diagnostic tools for early detection of malnutrition is necessary to prevent long-term complications to patients' health and well-being. Most of these tools are based on predefined questionnaires and consensus guidelines. The use of artificial intelligence (AI) allows for automated tools to detect malnutrition in an earlier stage to prevent long-term consequences. In this study, a systematic literature review was carried out with the goal of providing detailed information on what patient groups, screening tools, machine learning algorithms, data types, and variables are being used, as well as the current limitations and implementation stage of these AI-based tools. The results showed that a staggering majority exceeding 90% of all AI models go unused in day-to-day clinical practice. Furthermore, supervised learning models seemed to be the most popular type of learning. Alongside this, disease-related malnutrition was the most common category of malnutrition found in the analysis of all primary studies. This research provides a resource for researchers to identify directions for their research on the use of AI in malnutrition.

Indexed as

Artificial IntelligenceMalnutritionHumansMass ScreeningNutrition Assessmentdecision supportmachine learningmalnutritionnutritional assessmentnutritional screening toolpersonalized nutritionprecision nutrition

Identifiers

PMID38971229
PMCPMC11403436

What OpenQuestion holds

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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.