Journal of Radiology
Open Access

AI-Powered Radiology: Revolutionizing Diagnostics and Workflows

Dr. Sunita Rao^*

Department of Imaging Data Science, University of Colombo, Sri Lanka

Received Date: Dec 02, 2025 / Published Date: Dec 30, 2026

Citation: Rao DS (2025) AI-Powered Radiology: Revolutionizing Diagnostics and Workflows. J Radiol 14: 761.DOI: 10.4172/2167-7964.1000761

Copyright: © 2025 Dr. Sunita Rao 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.

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This work explores the multifaceted impact of data mining, artificial intelligence, and machine learning on modern radiology. We examine how these technologies enhance diagnostic accuracy, optimize clinical workflows, and enable personalized patient care through advanced image analysis, quantitative biomarker extraction, and predictive modeling. The integration of deep learning, natural language processing, radiomics, federated learning, and explainable AI is discussed, alongside the critical ethical considerations and challenges for responsible implementation in clinical practice. The potential for AI to improve efficiency, early detection, and patient outcomes in radiology is highlighted

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