Emergency Radiology: AI, Ultrasound, and Patient Outcomes
*Corresponding Author: Dr. Daniel Novak, Department of Emergency Radiology, Charles University, Czech Republic, Email: d.novak@emgrad.czReceived Date: Mar 03, 2025 / Published Date: Mar 31, 2025
Citation: Novak DD (2025) Emergency Radiology: AI, Ultrasound, and Patient Outcomes. J Radiol 14: 674DOI: 10.4172/2167-7964.1000674
Copyright: © 2025 Dr. Daniel Novak This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.
Abstract
This review consolidates recent advancements and established practices in emergency radiology. It examines the roles of artificial intelligence and machine learning in enhancing diagnostic capabilities and efficiency. The application of point-of-care ultrasound, alongside optimized imaging protocols for trauma, neurological emergencies, cardiopulmonary conditions, abdominal pain, and musculoskeletal injuries, is detailed. Furthermore, the importance of imaging in sepsis diagnosis and the imperative of radiation safety are discussed, collectively offering a current perspective on critical care imaging.
