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@ARTICLE{Psoroulas:298174,
      author       = {S. Psoroulas and A. Paunoiu and S. Corradini and J.
                      Hörner-Rieber$^*$ and S. Tanadini-Lang},
      title        = {{MR}-linac: role of artificial intelligence and
                      automation.},
      journal      = {Strahlentherapie und Onkologie},
      volume       = {201},
      number       = {3},
      issn         = {0179-7158},
      address      = {Heidelberg},
      publisher    = {Springer Medizin},
      reportid     = {DKFZ-2025-00193},
      pages        = {298-305},
      year         = {2025},
      note         = {2025 Mar;201(3):298-305},
      abstract     = {The integration of artificial intelligence (AI) into
                      radiotherapy has advanced significantly during the past 5
                      years, especially in terms of automating key processes like
                      organ at risk delineation and treatment planning. These
                      innovations have enhanced consistency, accuracy, and
                      efficiency in clinical practice. Magnetic resonance
                      (MR)-guided linear accelerators (MR-linacs) have greatly
                      improved treatment accuracy and real-time plan adaptation,
                      particularly for tumors near radiosensitive organs. Despite
                      these improvements, MR-guided radiotherapy (MRgRT) remains
                      labor intensive and time consuming, highlighting the need
                      for AI to streamline workflows and support rapid
                      decision-making. Synthetic CTs from MR images and automated
                      contouring and treatment planning will reduce manual
                      processes, thus optimizing treatment times and expanding
                      access to MR-linac technology. AI-driven quality assurance
                      will ensure patient safety by predicting machine errors and
                      validating treatment delivery. Advances in intrafractional
                      motion management will increase the accuracy of treatment,
                      and the integration of imaging biomarkers for outcome
                      prediction and early toxicity assessment will enable more
                      precise and effective treatment strategies.},
      subtyp        = {Review Article},
      keywords     = {Artificial intelligence (Other) / Automation (Other) /
                      Imaging biomarkers (Other) / Intrafractional motion
                      management (Other) / MR-guided radiation therapy (Other)},
      cin          = {E050},
      ddc          = {610},
      cid          = {I:(DE-He78)E050-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:39843783},
      doi          = {10.1007/s00066-024-02358-9},
      url          = {https://inrepo02.dkfz.de/record/298174},
}