Evidence map›Paper›PMID 40140092›Full record

ReviewCardiovascular and interventional radiology2025

IR-GPT: AI Foundation Models to Optimize Interventional Radiology.

Jacqueline L Brenner, James T Anibal, Lindsey A Hazen, Miranda J Song, Hannah B Huth, Daguang Xu, Sheng Xu, Bradford J Wood

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Cardiovascular and interventional radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Article
  5. 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

8 authors.

Jacqueline L Brenner *Center for Interventional Oncology, NIH Clinical Center, National Institutes of Health (NIH), Bethesda, USA.
James T Anibal *Center for Interventional Oncology, NIH Clinical Center, National Institutes of Health (NIH), Bethesda, USA. anibal.james@nih.gov.
Lindsey A HazenCenter for Interventional Oncology, NIH Clinical Center, National Institutes of Health (NIH), Bethesda, USA.
Miranda J SongCenter for Interventional Oncology, NIH Clinical Center, National Institutes of Health (NIH), Bethesda, USA.
Hannah B HuthCenter for Interventional Oncology, NIH Clinical Center, National Institutes of Health (NIH), Bethesda, USA.
Daguang XuNVIDIA, Inc, Santa Clara, USA.
Sheng XuCenter for Interventional Oncology, NIH Clinical Center, National Institutes of Health (NIH), Bethesda, USA.
Bradford J WoodCenter for Interventional Oncology, NIH Clinical Center, National Institutes of Health (NIH), Bethesda, USA.

Funding

Center for Interventional OncologyZIDBC011242 · NCI · DIVISION OF BASIC SCIENCES - NCI · PI WOOD, BRADFORD J · 2009 to 2025
$18.7M
Interventional OncologyZIACL040015 · CLC · CLINICAL CENTER · PI WOOD, BRADFORD · 2009 to 2025
$0k
Intramural NIH HHS ZIA CL040015Intramural NIH HHS ZID BC011242NIH Center for Interventional Oncology 1ZIDBC011242NIH Center for Interventional Oncology Z1A CL040015
6 · The paper itself

Abstract

Foundation artificial intelligence (AI) models are capable of complex tasks that involve text, medical images, and many other types of data, but have not yet been customized for procedural medicine. This report reviews prior work in deep learning related to interventional radiology (IR), identifying barriers to generalization and deployment at scale. Moreover, this report outlines the potential design of an "IR-GPT" foundation model to provide a unified platform for AI in IR, including data collection, annotation, and training methods-while also contextualizing challenges and highlighting potential downstream applications.

Indexed as

Artificial IntelligenceDeep LearningRadiology, InterventionalHumansArtificial IntelligenceFoundation modelsMulitmodal dataProcedural optimization

Identifiers

PMID40140092
PMCPMC12052823

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.