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©The Author(s) 2026. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Radiol. Jan 28, 2026; 18(1): 117814
Published online Jan 28, 2026. doi: 10.4329/wjr.v18.i1.117814
Published online Jan 28, 2026. doi: 10.4329/wjr.v18.i1.117814
Expanding the applications of artificial intelligence in emergency radiology: Advancing precision medicine and resource efficiency
Zhen-Xing He, Jie Wang, Clinical Institute of Shantou University Medical College, The Third People’s Hospital of Longgang, Shenzhen 518172, Guangdong Province, China
Jian-She Yang, Department of Nuclear Medicine and Oncology Research, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai 200072, China
Co-first authors: Zhen-Xing He and Jie Wang.
Author contributions: He ZX and Wang J contributed to the discussion and design of the manuscript, and they contributed equally to this manuscript and are co-first authors; He ZX, Wang J, and Yang JS contributed to the writing and editing of the manuscript, illustration, and literature review; Yang JS designed the overall concept and outline of the manuscript. All authors approval the final manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
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: Jian-She Yang, PhD, Academic Fellow, Chairman, Dean, Professor, Department of Nuclear Medicine and Oncology Research, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, No. 301 Yanchang Road, Shanghai 200072, China.
Received: December 17, 2025
Revised: January 12, 2026
Accepted: January 15, 2026
Published online: January 28, 2026
Processing time: 40 Days and 22.2 Hours
Revised: January 12, 2026
Accepted: January 15, 2026
Published online: January 28, 2026
Processing time: 40 Days and 22.2 Hours
Core Tip
Core Tip: With emerging technologies such as quantum computing and federated learning poised to revolutionize diagnostic capabilities, the future of artificial intelligence in emergency radiology is promising. These innovations will enable the rapid processing of complex imaging data and support precision medicine tailored to individual patient needs. By overcoming current challenges and leveraging current and future advancements in artificial intelligence, emergency radiology will achieve new heights in precision medicine, ultimately enhancing patient care and operational efficiency.