MR-linac: role of artificial intelligence and automation

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.

Keywords: Artificial intelligence; Automation; Imaging biomarkers; Intrafractional motion management; MR-guided radiation therapy.

Fig. 1

Potential applications of artificial intelligence (AI) in the MR-linac workflow: the potential of AI in magnetic resonance-guided radiotherapy for selecting patients, supporting the adaptive process, ensuring the quality of the treatment, managing motion during treatment delivery, and deriving predictive and prognostic biomarkers