AI Revolutionizes Medical Imaging: Precision and Automation
*Corresponding Author: Dr. Amir Hosseini, Department of AI Image Segmentation, Shiraz University, Iran, Email: a.hosseini@aiseg.irReceived Date: Dec 02, 2025 / Published Date: Dec 30, 2025
Citation: Hosseini DA (2025) AI Revolutionizes Medical Imaging: Precision and Automation. J Radiol 14: 755.DOI: 10.4172/2167-7964.1000755
Copyright: © 2025 Dr. Amir Hosseini 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
Deep learning models, especially CNNs, are revolutionizing medical image segmentation in radiology, enhancing diagnostic precision and automating tasks. Applications range from brain tumor segmentation and lung nodule detection to radiotherapy planning. Emerging technologies like transformers and federated learning, alongside Explainable AI (XAI), are addressing challenges such as data limitations and clinical trust. Integration into quantitative imaging analysis is a key driver, moving towards data-driven insights.
