ArticleWorld journal of radiology2025
Radiologists' perspectives on the use of artificial intelligence in emergency radiology: A pilot survey.
Article in World journal of radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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.
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Who cites it
2 citing papers in PubMed.
- Artificial Intelligence for Detection and Characterisation of Bone Metastases on MRI: A Scoping Review.Cancers · 2026Review
- Expanding the applications of artificial intelligence in emergency radiology: Advancing precision medicine and resource efficiency.World journal of radiology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) is increasingly being explored in radiology, including its potential role in emergency imaging settings. However, global perspectives on AI adoption, usefulness, and limitations among emergency radiologists remain underexplored.
aimTo assess awareness, usage, perceived benefits, and limitations of AI tools among radiologists practicing emergency radiology worldwide.
methodsA 16-question survey was distributed globally between October 24, 2024, and August 4, 2025, targeting radiologists working in academic, community, and private settings who practice emergency radiology as a primary or secondary subspecialty. The survey was disseminated
resultsAI awareness was high (93%), but frequent clinical use was reported by only 28%. Daily use of AI in emergent imaging was limited to 23% of respondents. The majority anticipated AI becoming essential within five years (68%), and 51% believed AI would replace certain radiological tasks. Image interpretation and acquisition were the most common AI applications. Key perceived benefits included improved diagnostic accuracy and increased efficiency, while concerns included limited accuracy, integration difficulties, and cost. Trust in AI varied by experience, with less experienced radiologists viewed as more trusting.
conclusionWhile emergency radiologists globally recognize AI's potential, significant barriers to its routine adoption remain. Addressing issues of trust, cost, accuracy, and workflow integration is essential to unlock AI's full utility in emergency radiology.
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Registered trials
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.