%0 Journal Article
%T Artificial intelligence for treatment delivery: image-guided radiotherapy.
%A Rabe M
%A Kurz C
%A Thummerer A
%A Landry G
%J Strahlenther Onkol
%V 0
%N 0
%D 2024 Aug 13
%M 39138806
%F 4.033
%R 10.1007/s00066-024-02277-9
%X 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.