Evidence map›Paper›PMID 40873379›Full record

ArticleKorean journal of radiology2025

Uncover This Tech Term: Agentic Artificial Intelligence in Radiology.

Shahriar Faghani, Mana Moassefi, Pouria Rouzrokh, Bardia Khosravi, Bradley J Erickson

Abstract readEditorial
PubMed Full text at PMC Publisher
In one paragraph

Article in Korean journal of radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Applications of artificial intelligence in nuclear medicine.Zeitschrift fur medizinische Physik · 2026
    Review
  2. Article
  3. Review
  4. Review
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

5 authors.

Shahriar FaghaniDepartment of Radiology, Artificial Intelligence Laboratory, Mayo Clinic, Rochester, MN, USA.ORCID https://orcid.org/0000-0003-3275-2971
Mana MoassefiDepartment of Radiology, Artificial Intelligence Laboratory, Mayo Clinic, Rochester, MN, USA.ORCID https://orcid.org/0000-0002-0111-7791
Pouria RouzrokhDepartment of Radiology, Artificial Intelligence Laboratory, Mayo Clinic, Rochester, MN, USA.ORCID https://orcid.org/0000-0003-4664-0751
Bardia KhosraviDepartment of Radiology, Artificial Intelligence Laboratory, Mayo Clinic, Rochester, MN, USA.ORCID https://orcid.org/0000-0002-8024-339X
Bradley J EricksonDepartment of Radiology, Artificial Intelligence Laboratory, Mayo Clinic, Rochester, MN, USA. bje@mayo.edu.ORCID https://orcid.org/0000-0001-7926-6095

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

Agentic artificial intelligenceDeep learningFoundational modelLarge language modelRadiology

Identifiers

PMID40873379
PMCPMC12394819

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.