Radiology Advances Podcast | RSNA

• RadGPT Delivers a Smarter Approach to Knee Imaging

  This episode explores Radiology Advances research on RadGPT—a hybrid AI system combining image analysis with a language model to interpret knee radiographs. Built on 77,000 images, the system incorporates mandatory human review, dramatically improving diagnostic accuracy and report quality. Host commentary highlights its potential as a diagnostic assistant for trainees and an efficiency tool for experts. Visual-language artificial intelligence system for knee radiograph diagnosis and interpretation: a collaborative system with humans. He et al. Radiology Advances, 2025, 2(5), umaf027.  

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• Deep Learning for Faster Neuro MRI

  This episode covers a study from Radiology Advances evaluating deep learning–accelerated MRI across routine neuroradiology exams. Using Siemens' Deep Resolve, scan times were cut by over 50% without sacrificing diagnostic image quality. Host commentary explores reader preferences, artifacts, and when DL-MRI may be best suited for clinical use. Deep learning MRI halves scan time and preserves image quality across routine neuroradiologic examinations. Lyo et al. Radiology Advances, 2025, 2(5), umaf029.

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• CT as a Noninvasive Alternative for Lung Shunt Fraction Estimation

  This episode discusses a study from Radiology Advances evaluating contrast-enhanced CT as a non-invasive alternative for lung shunt fraction (LSF) estimation in hepatic radioembolization to the current standard, 99mTc-MAA nuclear medicine imaging. The proposed CT-based method showed strong correlation with standard MAA-based LSF, offering a faster, safer, and potentially more accurate planning approach without compromising clinical decision-making. Contrast-enhanced CT as a non-invasive alternative for lung shunt fraction estimation in hepatic transarterial radioembolization. Mehadji et al. Radiology Advances, 2025, 2(4), umaf025. 

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• Advancing MRI Efficiency in Memory Disorders

  This episode covers a study in Radiology Advances evaluating deep learning–accelerated T1 MPRAGE MRI in patients with memory loss. The approach cut scan time by more than half while preserving image quality and measurement accuracy—offering faster, more comfortable imaging for dementia care and longitudinal follow-up. Deep-learning-accelerated T1-MPRAGE MRI for  quantification and visual grading of cerebral volume in memory loss patients. Gil et al. Radiology Advances, 2025, 2(4), umaf022

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• Automating Myocardial Infarct Segmentation

  This episode spotlights a study from Radiology Advances introducing a fully automated deep learning pipeline for myocardial infarct segmentation on late gadolinium enhancement cardiac MRI. Developed at the Medical University of Innsbruck, the model showed near-perfect agreement with human experts and even outperformed manual segmentations in blinded qualitative review. Deep learning pipeline for fully automated myocardial infarct segmentation from clinical cardiac MR scans. Schwab et al. Radiology Advances, 2025, 2(4), umaf023.  

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