Evidence map›Paper›PMID 42032303›Full record

ReviewCardiovascular and interventional radiology2026

A Review on Artificial Intelligence as a Solution to Burnout in Interventional Radiology.

Henry Zhang, Aria Torkpour, Boroumand Zeidaabadi, Nader Ashraf, Anousha Yazdabadi, Shams I Iqbal, Hamed Asadi, Roberto Luigi Cazzato, Robert Morgan, Behnam Shaygi

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Cardiovascular and interventional radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Henry Zhang *Imperial College London, London, SW7 2AZ, UK. henry.zhang20@imperial.ac.uk.ORCID http://orcid.org/0009-0005-3505-6455
Aria Torkpour *Imperial College London, London, SW7 2AZ, UK.
Boroumand Zeidaabadi *Imperial College London, London, SW7 2AZ, UK.
Nader AshrafCollege of Medicine, Alfaisal University, Takhasusi Road, P.O. Box 50927, Riyadh, Saudi Arabia.
Anousha YazdabadiEastern Health Clinical School, Monash University and Eastern Health, Melbourne, Australia.
Shams I IqbalMassachusetts General Hospital, Boston Massachusetts, MA, 02114, USA.
Hamed AsadiNeurointerventional Radiology Unit, Monash Health, Melbourne, Australia.
Roberto Luigi CazzatoDepartment of Interventional Radiology, University Hospital of Strasbourg, Strasbourg, France.
Robert MorganSt George's University Hospitals NHS Foundation Trust and St George's, Blackshaw Road, London, SW17 0QT, UK.
Behnam ShaygiImperial College London, London, SW7 2AZ, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeWe review burnout risk factors in interventional radiology (IR) and explore how artificial intelligence (AI) would address burnout from a workplace aspect. MATERIALS AND

methodsWe performed a literature search on PubMed on risk factors for burnout in interventional radiology and AI tools to address burnout challenges.

resultsIR specialists face burnout risk at personal, workplace and system levels. AI could identify burnout using demographic data and free text, alleviate administrative workload, and manage workflow. AI could also enhance procedural efficiency via automated navigation systems, reducing stress from radiation exposure. Future directions include enhanced burnout identification and medical coding for access to longitudinal data.

conclusionAI may be a solution to addressing specific burnout risk factors in interventional radiology. LEVEL OF EVIDENCE: No level of evidence. Review Article.

Indexed as

Artificial IntelligenceBurnout, ProfessionalRadiology, InterventionalHumansRisk FactorsWorkloadArtificial intelligenceBurnoutInterventional radiology

Identifiers

PMID42032303
PMCPMC13597660

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.