AI in Radiology: Enhancing Accuracy, Efficiency, and Trust
*Corresponding Author: Dr. Kevin Marshall, Department of Clinical Decision Support, University of Calgary, Canada, Email: k.marshall@raddss.caReceived Date: Sep 03, 2025 / Published Date: Sep 30, 2025
Citation: Marshall DK (2025) AI in Radiology: Enhancing Accuracy, Efficiency, and Trust. J Radiol 14: 730DOI: 10.4172/2167-7964.1000730
Copyright: © 2025 Dr. Kevin Marshall 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
Radiology decision support systems (RDSS), leveraging AI and machine learning, are revolutionizing medical image interpretation by enhancing diagnostic accuracy and efficiency. These systems aid radiologists by identifying subtle abnormalities, optimizing workflows, and providing actionable insights. Integration with PACS and EHR is crucial. Challenges include generalizability, data requirements, and ethical considerations like bias. Explainable AI is key for trust. Successful deployment necessitates multidisciplinary efforts, user-centric design, rigorous validation, and continuous adaptation to improve patient outcomes
