Unsupervised anomaly detection framework for pelvic and brain MRI reports AUC 0.97 and 0.81 on synthetic and clinical anomalies with spatial localization.
Artificial intelligence in radiation oncology
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This review summarizes how large language models are being used for workflow automation, clinical decision support, and patient engagement in radiation oncology.
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Catching MRI outliers: unsupervised detection and localization of MRI artefacts and clinical anomalies using deep learning
Unsupervised anomaly detection framework for pelvic and brain MRI reports AUC 0.97 and 0.81 on synthetic and clinical anomalies with spatial localization.
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Applications of Large Language Models in Radiation Oncology: From Workflow Automation to Clinical Intelligence
This review summarizes how large language models are being used for workflow automation, clinical decision support, and patient engagement in radiation oncology.