Evidence map›Paper›PMID 41891986›Full record

ReviewDiseases (Basel, Switzerland)2026

Tracking the Metabolites of Health and Disease Using Artificial Intelligence.

Ahmed Fadiel, Kenneth D Eichenbaum, Aya Hassouneh, Kunle Odunsi

Abstract readReview
In one paragraph

Review in Diseases (Basel, Switzerland), 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

4 authors.

Ahmed FadielComputational Oncology Unit, University of Chicago Medicine Comprehensive Cancer Center, 900 E 57th St, KCBD Bldg., Chicago, IL 60637, USA.
Kenneth D EichenbaumDepartment of Anesthesiology, Oakland University William Beaumont School of Medicine, Rochester, MI 48309, USA.ORCID 0000-0001-9634-8295
Aya HassounehElectrical and Computer Engineering, Western Michigan University, 1903 W. Michigan Ave., Kalamazoo, MI 49008, USA.ORCID 0000-0003-4617-4535
Kunle OdunsiUniversity of Chicago Medicine Comprehensive Cancer Center, 5841 South Maryland Avenue, MC1140, Chicago, IL 60637, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Using AI to analyze metabolite profiles provides critical insights into health, aging, and disease. Metabolomic signatures reveal how lifestyle and therapy impact organ function and cancer progression. This review highlights emerging toolkits for high-throughput data analysis, emphasizing their integration with other omics. Advanced AI approaches facilitate metabolic pathway mapping and accelerate biomarker discovery. By combining AI with multi-omics, researchers can optimize interventions and enhance precision medicine. This article serves as a resource demonstrating AI's potential in diagnostics and drug discovery.

Indexed as

artificial intelligencebioinformaticscancerdrug development pipelinesmetabolomicsmulti-omics

Identifiers

PMID41891986
PMCPMC13025964

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.