He ZX, Wang J, Yang JS. Expanding the applications of artificial intelligence in emergency radiology: Advancing precision medicine and resource efficiency. World J Radiol 2026; 18(1): 117814 [DOI: 10.4329/wjr.v18.i1.117814]
Corresponding Author of This Article
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. 2305499@tongji.edu.cn
Research Domain of This Article
Radiology, Nuclear Medicine & Medical Imaging
Article-Type of This Article
Editorial
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
Jan 28, 2026 (publication date) through Jan 28, 2026
Times Cited of This Article
Times Cited (0)
Journal Information of This Article
Publication Name
World Journal of Radiology
ISSN
1949-8470
Publisher of This Article
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
Share the Article
He ZX, Wang J, Yang JS. Expanding the applications of artificial intelligence in emergency radiology: Advancing precision medicine and resource efficiency. World J Radiol 2026; 18(1): 117814 [DOI: 10.4329/wjr.v18.i1.117814]
World J Radiol. Jan 28, 2026; 18(1): 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, Jian-She Yang
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. 2305499@tongji.edu.cn
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
Abstract
Owing to their swift, precise, and tireless capabilities, artificial intelligence (AI) applications in emergency radiology are becoming powerful tools for radiologists. These applications, which are useful for improving diagnostic efficiency, are also a core engine driving the entire field of emergency medicine toward higher levels of precision, personalization, and efficiency. The integration of AI into emergency radiology thus represents a transformative advancement in precision medicine. We explore herein the expanding applications of AI in emergency radiology, focusing on their potential to enhance diagnostic accuracy, streamline workflows, and improve patient outcomes. By analyzing its current utilization and future directions, we demonstrate how AI is revolutionizing emergency care through intelligent image analysis and decision support systems. Although certain challenges remain, including data security, model interpretability, and clinical implementation standards, the immense potential of AI to reshape emergency workflows, promote precision medicine, and improve patient outcomes is unmistakable.
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.
