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Review
. 2025 Mar;201(3):283-297.
doi: 10.1007/s00066-024-02277-9. Epub 2024 Aug 13.

Artificial intelligence for treatment delivery: image-guided radiotherapy

Moritz Rabe  1 , Christopher Kurz  1 , Adrian Thummerer  1 , Guillaume Landry  2   3   4
Affiliations
Review

Artificial intelligence for treatment delivery: image-guided radiotherapy

Moritz Rabe et al. Strahlenther Onkol. 2025 Mar.

Abstract

Radiation therapy (RT) is a highly digitized field relying heavily on computational methods and, as such, has a high affinity for the automation potential afforded by modern artificial intelligence (AI). This is particularly relevant where imaging is concerned and is especially so during image-guided RT (IGRT). With the advent of online adaptive RT (ART) workflows at magnetic resonance (MR) linear accelerators (linacs) and at cone-beam computed tomography (CBCT) linacs, the need for automation is further increased. AI as applied to modern IGRT is thus one area of RT where we can expect important developments in the near future. In this review article, after outlining modern IGRT and online ART workflows, we cover the role of AI in CBCT and MRI correction for dose calculation, auto-segmentation on IGRT imaging, motion management, and response assessment based on in-room imaging.

Keywords: Automatic segmentation; Deep learning; Motion management; Online adaptive radiation therapy; Synthetic computed tomography.

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Conflict of interest statement

Conflict of interest: The Department of Radiation Oncology of the LMU University Hospital of the LMU Munich has research agreements with Elekta, Brainlab, and C‑RAD.

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