AI and NLP Enhance Radiology Reporting Systems
*Corresponding Author: Dr. Tomasz Lewandowski, Department of Radiology Informatics, Gda艅sk Medical University, Poland, Email: t.lewandowski@radrep.plReceived Date: Sep 03, 2025 / Published Date: Sep 30, 2025
Citation: Lewandowski DT (2025) AI and NLP Enhance Radiology Reporting Systems. J Radiol 14: 726.DOI: 10.4172/2167-7964.1000726
Copyright: © 2025 Dr. Tomasz Lewandowski 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 reporting systems are crucial for efficient and accurate medical image interpretation. Advancements in natural language processing (NLP), artificial intelligence (AI), and integration with PACS and EMRs are enhancing diagnostic accuracy and workflow efficiency. Structured reporting and interoperability standards are vital for clear communication and unified patient data. Decision support tools, voice recognition, and user-friendly interfaces further optimize reporting processes. Ensuring data security and privacy remains paramount, with a focus on compliance with regulations.
