Radiologists’ perspectives on the use of artificial intelligence in emergency radiology: A pilot survey

doi:10.4329/wjr.v17.i12.115388

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World Journal of Radiology

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1949-8470

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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World J Radiol. Dec 28, 2025; 17(12): 115388
Published online Dec 28, 2025. doi: 10.4329/wjr.v17.i12.115388

Radiologists’ perspectives on the use of artificial intelligence in emergency radiology: A pilot survey

Francesca R Centini, Daniil Fedorov, Arosh S Perera Molligoda Arachchige

Francesca R Centini, Daniil Fedorov, Arosh S Perera Molligoda Arachchige, Faculty of Medicine, Humanitas University, Pieve Emanuele 20072, Lombardy, Italy

Co-first authors: Francesca R Centini and Arosh S Perera Molligoda Arachchige.

Author contributions: Centini FR, Fedorov D, and Perera Molligoda Arachchige AS carried out methodology, data collection, analysis, and did writing review and editing; Centini FR and Perera Molligoda Arachchige AS contributed equally to this article, they are the co-first authors of this manuscript; Perera Molligoda Arachchige AS led visualization, supervision, project administration, and writing the original draft; and all authors thoroughly reviewed and endorsed the final manuscript.

Institutional review board statement: Institutional Review Board approval was not applicable for this study because no identifiable human data were collected, participation was voluntary and anonymous, and the research involved minimal risk.

Informed consent statement: Written informed consent was not required for this study because participation was voluntary, responses were anonymous, and no identifiable human data were collected. Consent was implied upon completion of the survey.

Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.

STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.

Data sharing statement: Data generated has been shown in the results section of this paper.

Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/

Corresponding author: Arosh S Perera Molligoda Arachchige, MD, Faculty of Medicine, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele 20072, Lombardy, Italy. aroshperera@outlook.it

Received: October 17, 2025
Revised: October 22, 2025
Accepted: December 3, 2025
Published online: December 28, 2025
Processing time: 71 Days and 22.6 Hours

Abstract

BACKGROUND

Artificial 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.

AIM

To assess awareness, usage, perceived benefits, and limitations of AI tools among radiologists practicing emergency radiology worldwide.

METHODS

A 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 via direct emails extracted using automated and manual methods from recent publications in major radiology journals. A total of 57 responses were collected.

RESULTS

AI 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.

CONCLUSION

While 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.

Keywords: Emergency radiology; Demographics; Limitations; Applications; Survey

Core Tip: This study addresses the limited data on how emergency radiologists worldwide perceive, use, and trust artificial intelligence (AI) tools in clinical workflows, highlighting an unmet need for global insight. Surveying 57 emergency radiologists globally, we found high AI awareness (93%) but limited frequent use (28%), with the United States and Italy as leading respondent countries. Understanding emergency radiologists’ attitudes towards AI guides tailored implementation, improving diagnostic accuracy, workflow efficiency, and ultimately patient care in emergency imaging.