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©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Radiol. Dec 28, 2025; 17(12): 115388
Published online Dec 28, 2025. doi: 10.4329/wjr.v17.i12.115388
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, 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.
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
Revised: October 22, 2025
Accepted: December 3, 2025
Published online: December 28, 2025
Processing time: 71 Days and 22.6 Hours
Core Tip
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.