Evidence map›Paper›PMID 40560391›Full record

ArticleCardiovascular and interventional radiology2025

Interventional Radiology Reporting Standards and Checklist for Artificial Intelligence Research Evaluation (iCARE).

James T Anibal, Hannah B Huth, Tom Boeken, Dania Daye, Judy Gichoya, Fernando Gómez Muñoz, Julius Chapiro, Bradford J Wood, Daniel Y Sze, Klaus Hausegger

Erratum issuedAbstract read
PubMed Publisher
In one paragraph

Article in Cardiovascular and interventional radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. A Promptable 3D-CT Foundation Model-Based Approach for Pulmonary Embolism.Cardiovascular and interventional radiology · 2026
    Article
  2. Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

James T AnibalCenter for Interventional Oncology, NIH Clinical Center, National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health (NIH), Bethesda, MD, 20892, USA. anibal.james@nih.gov.
Hannah B HuthCenter for Interventional Oncology, NIH Clinical Center, National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health (NIH), Bethesda, MD, 20892, USA.
Tom BoekenUniversité Paris Cité, Faculté de Médecine, 75006, Paris, France.
Dania DayeMassachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Judy GichoyaDepartment of Radiology, Emory University School of Medicine, Atlanta, GA, USA.
Fernando Gómez MuñozImagen Médica, Hospital Universitario y Politécnico La Fe/Antoni Van Leeuwenhoek-NKI, Amsterdam, The Netherlands.
Julius ChapiroYale School of Medicine, New Haven, CT, USA.
Bradford J WoodCenter for Interventional Oncology, NIH Clinical Center, National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health (NIH), Bethesda, MD, 20892, USA.
Daniel Y SzeJournal for Vascular and Interventional Radiology, Philadelphia, USA.
Klaus HauseggerCardioVascular and Interventional Radiology, Vienna, Austria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As artificial intelligence (AI) becomes increasingly prevalent within interventional radiology (IR) research and clinical practice, steps must be taken to ensure the robustness of novel technological systems presented in peer-reviewed journals. This report introduces comprehensive standards and an evaluation checklist (iCARE) that covers the application of modern AI methods in IR-specific contexts. The iCARE checklist encompasses the full "code-to-clinic" pipeline of AI development, including dataset curation, pre-training, task-specific training, explainability, privacy protection, bias mitigation, reproducibility, and model deployment. The iCARE checklist aims to support the development of safe, generalizable technologies for enhancing IR workflows, the delivery of care, and patient outcomes.

Indexed as

Artificial IntelligenceChecklistRadiology, InterventionalHumansArtificial intelligence (AI)Evaluation checklistInterventional radiologyStandards

Identifiers

PMID40560391

What OpenQuestion holds

Textmetadata
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.