Evidence map›Paper›PMID 41685266›Full record

ReviewFrontiers in aging2026

A comprehensive review of artificial intelligence as a catalyst in aging research: insights, gaps and future perspectives.

Tasnuva Binte Mahbub, Parsa Safaeian, Salman Sohrabi

Abstract readReview
In one paragraph

Review in Frontiers in aging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

3 authors.

Tasnuva Binte MahbubDepartment of Bioengineering, University of Texas at Arlington, Arlington, TX, United States.
Parsa SafaeianDepartment of Bioengineering, University of Texas at Arlington, Arlington, TX, United States.
Salman SohrabiDepartment of Bioengineering, University of Texas at Arlington, Arlington, TX, United States.

Funding

Decoupling reproductive and somatic aging using novel C. elegans technologyR15AG089221 · NIA · UNIVERSITY OF TEXAS ARLINGTON · PI Salman Sohrabi · 2025 to 2026
$513k
NIA NIH HHS R15 AG089221
6 · The paper itself

Abstract

Aging is driven by interconnected genetic, epigenetic, molecular, and physiological processes spanning from unicellular to organismal levels. The surge in high-throughput data, from clinical and imaging to multi-omics, has outpaced traditional analysis methods; driving the integration of artificial intelligence (AI) into aging research. This comprehensive review examines the application of machine learning, deep learning, and computer vision across four canonical aging models (yeast,

Indexed as

agingartificial intelligenceclinical translatabilityin-vivo validationmodel organisms

Identifiers

PMID41685266
PMCPMC12891069

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.