Catching magnetic resonance imaging outliers in artificial intelligence-supported radiotherapy workflows: unsupervised detection and localization of image anomalies using deep learning

Researchers have developed a deep learning framework designed to automatically detect and localize unexpected anomalies in magnetic resonance imaging used for radiotherapy. By identifying out-of-distribution data that can disrupt clinical AI workflows, this tool helps ensure that automated treatment planning remains accurate and reliable.