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EDITORIAL article
Front. Med.
Sec. Intensive Care Medicine and Anesthesiology
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1593416
This article is part of the Research Topic Clinical Application of Artificial Intelligence in Emergency and Critical Care Medicine, Volume V View all 15 articles
Editorial: Clinical application of artificial intelligence in emergency and critical care medicine, volume V
Provisionally accepted- 1 Anhui Medical University, Hefei, Anhui Province, China
- 2 Department of Emergency Medicine, Sir Run Run Shaw Hospital, Hangzhou, China
- 3 Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, Minnesota, United States
- 4 Department of Research, WellSpan Health, York, PA, United States
- 5 Department of Computer Science and Engineering, College of Engineering, The Ohio State University, Columbus, Ohio, United States
- 6 Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, Ohio, United States
- 7 Department of Surgery, State University of New York Upstate Medical University, 750 East Adams Street, Syracuse, NY, 13210, United States
- 8 Sepsis Interdisciplinary Research Center, State University of New York Upstate Medical University, 766 Irving Avenue, Syracuse, NY, 13210, United States
- 9 School of Medicine, Shaoxing University, Shaoxing, Zhejiang Province, China
- 10 Key Laboratory of Precision Medicine in Diagnosis and Monitoring Research of Zhejiang Province, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, Jiangsu Province, China
- 11 Longquan Industrial Innovation Research Institute, Lishui, China
3 AI also demonstrates immense potential in personalized strategies. Traditional clinical 77 guidelines struggle to account for the unique physiological characteristics of individual patients, 78 whereas AI-driven approaches offer the ability to support clinicians in more precise 79 tailored treatment plans(4). Nevertheless, while AI has demonstrated performance, it is crucial to recognize its limitations and reinforce validation and regulatory oversight in real-world 81 applications to ensure tangible patient benefits(5).
Keywords: artificial intelligence, prediction, machine learning, Critical Care, Treatment Decision-making
Received: 14 Mar 2025; Accepted: 31 Mar 2025.
Copyright: © 2025 Wang, Kashyap, Zhang, Meng and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Zhongheng Zhang, School of Medicine, Shaoxing University, Shaoxing, Zhejiang Province, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.