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Vorbach, S.M.* ; Combs, S.E. ; Wiestler, B.* ; Peeken, J.C.

Image-Guided Radiooncology: The Potential of Artificial Intelligence in Clinical Application.

In: Molecular Imaging in Oncology. Springer, 2026. 835-858 (Recent Results Cancer Res. ; 225)
DOI
Defining artificial intelligence (AI) remains a complex challenge, given its rapidly evolving nature. Nevertheless, one broadly accepted definition—endorsed by the European Union—describes AI as a suite of algorithms that learn from data to make predictions. Since the early 2010s, AI has emerged as a highly effective approach for analyzing, processing, and even generating medical image data. In this chapter, we provide an overview of key AI paradigms—radiomics, deep learning (DL), and foundation models—and examine their current and potential applications in medical image analysis within radiation oncology. We focus on topics, such as image classification, treatment planning, and response assessment, as well as novel strategies involving vision language models (VLMs). Through this exploration, we offer insights into how AI is transforming clinical workflows and shaping future directions in radiation oncology.
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Publication type Article: Periodical or book chapter
Keywords Adaptive Radiotherapy ; Artificial Intelligence ; Deep Learning ; Foundation Models ; Image Segmentation ; Medical Imaging ; Radiation Oncology ; Radiomics ; Vision-language Models
ISSN (print) / ISBN 0080-0015
Book Volume Title Molecular Imaging in Oncology
Journal Recent Results in Cancer Research
Quellenangaben Volume: 225, Issue: , Pages: 835-858 Article Number: , Supplement: ,
Publisher Springer
Reviewing status Peer reviewed
Institute(s) Institute of Radiation Medicine (IRM)