Radiation Oncology: AI Drives Precision, Personalization
*Corresponding Author: Martin Skov, Copenhagen Medical University, Denmark, Email: m.skov@copenhagenmed.dkReceived Date: Sep 01, 2025 / Accepted Date: Sep 29, 2025 / Published Date: Sep 29, 2025
Citation: Skov M (2025) Radiation Oncology: AI Drives Precision, Personalization. jcd 09: 314.DOI: 10.4172/2476-2253.1000314
Copyright: © 2025 Martin Skov 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
Radiation therapy is rapidly advancing through deep learning for automated segmentation, enhancing planning efficiency and
accuracy. Adaptive proton therapy and online adaptive radiotherapy provide personalized, robust dose delivery despite anatomi
cal changes. Robust optimization and patient-specific quality assurance are vital for safety. Knowledge-based planning automates
plan generation, while 4D-CT improves motion management. \textit{Emerging radiomics offers predictive insights for personalized
care. Artificial Intelligence} (AI) is transforming clinical radiation oncology, promising enhanced efficiency, precision, and patient
outcomes.
