Evidence map›Paper›PMID 40576669›Full record

ReviewAbdominal radiology (New York)2026

Practical applications of AI in body imaging.

Benjamin M Mervak, Jessica G Fried, Julian Neshewat, Ashish P Wasnik

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Abdominal radiology (New York), 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

4 authors.

Benjamin M MervakUniversity of Michigan-Ann Arbor, Ann Arbor, USA.
Jessica G FriedUniversity of Michigan-Ann Arbor, Ann Arbor, USA.
Julian NeshewatUniversity of Michigan-Ann Arbor, Ann Arbor, USA.
Ashish P WasnikUniversity of Michigan-Ann Arbor, Ann Arbor, USA. ashishw@med.umich.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) algorithms and deep learning continue to change the landscape of radiology. New algorithms promise to enhance diagnostic accuracy, improve workflow efficiency, and automate repetitive tasks. This article provides a narrative review of the FDA-cleared AI algorithms which are commercially available in the United States as of late 2024 and targeted toward assessment of abdominopelvic organs and related diseases, evaluates potential advantages of using AI, and suggests future directions for the field.

Indexed as

Artificial IntelligenceDiagnostic ImagingImage Interpretation, Computer-AssistedAlgorithmsDeep LearningHumansAlgorithmsArtificial intelligenceDeep learningRadiologyWorkflow

Identifiers

PMID40576669
PMCPMC12830429

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.