A podcast showcasing articles from the Radiology Advances journal.
Podcast Team Lead Podcast Editor- Diego Lopez-Gonzalez, MD, MPH,
Trainee Editors- Nelson Gil, MD, PhD and Luca Salhöfer, MD
Radiology Advances Podcast | RSNA
Claim This Podcastby The Radiological Society of North America
5.0(3 reviews)
23 episodes
Updated Daily
Accepts GuestsHas Sponsors
9
Podcast Authority
BetaPoorBased on show quality, social media presence, reviews, charts, and more
Quality6
Social0
YouTube0
Engagement32
Podcast Overview
A podcast showcasing articles from the Radiology Advances journal. Podcast Team Lead Podcast Editor- Diego Lopez-Gonzalez, MD, MPH, Trainee Editors- Nelson Gil, MD, PhD and Luca Salhöfer, MD
Language
🇺🇲
Publishing Since
7/2/2025
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9
Podcast Authority
BetaPoorBased on show quality, social media presence, reviews, charts, and more
Quality6
Social0
YouTube0
Engagement32
4
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2
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13
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excellent
Episode Length
10 minutes
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good
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Every 13 days
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needs improvement
Show Notes Quality
2.0/5
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Recent Episodes
June 10, 2026
Episode 23: Predicting severe pancreatitis from admission CT with deep learning
<p>This episode discusses a study from New York University evaluating whether deep learning can predict acute pancreatitis severity from contrast-enhanced CT acquired within 24 hours of admission. Using self-supervised pretraining on about 12,000 unlabeled scans followed by supervised fine-tuning, the model achieved an AUROC near 0.89 for severe pancreatitis on both an internal NYU test set and an external multicenter Hungarian cohort of 518 patients, outperforming traditional clinical and imaging-based scoring systems. The work suggests that opportunistic AI triage on routinely acquired CT could support earlier, more accurate risk stratification in the emergency department.</p> <p><a href="https://doi.org/10.1093/radadv/umag020">Deep learning-based prediction of acute pancreatitis severity from abdominal CT with multicenter external validation. Xu et al. Radiology Advances, 2026, 3, umag020 </a></p>
May 20, 2026
Episode 22: Can LLM-generated summaries help patients understand lung cancer screening reports?
<p>This episode discusses a study from the University of California, San Francisco in the United States that tested whether GPT-4o-generated patient-friendly summaries improve comprehension of lung cancer screening CT reports. In a within-subjects survey of 1,815 adults across Lung-RADS 1, 2S, and 4B vignettes, the summaries significantly improved objective comprehension and reduced anxiety for all three report types. Largest gains were in participants with low self-rated English and health literacy. These findings support using LLM summariesas a potential health-equity tool, while highlighting the unmet patient need for personalized next-steps guidance.</p> <p><a href="https://doi.org/10.1093/radadv/umag008">Self-reported comprehension of large language model-generated summaries of lung cancer screening reports: a vignette survey. Serna et al. Radiology Advances, 2026, 3, umag008. </a></p>
May 6, 2026
Episode 21: Can AI catch cardiomegaly on chest CTs ordered for other reasons?
<p>This episode explores a study from the University of Texas Southwestern Medical Center and MD Anderson Cancer Center in the United States that clinically validates an FDA-cleared AI tool for measuring total cardiac volume on non-contrast, non-gated chest CT. Across 307 patients with paired echocardiography, the AI discriminated normal from abnormal cardiac volume with an AUC of 0.81 in men and 0.77 in women, and far outperformed routine radiologist sensitivity for cardiomegaly. The tool offers a tunable, reproducible opportunistic screening layer on chest CT's already being performed.</p> <p><a href="https://doi.org/10.1093/radadv/umag013">Radiology Advances, 2026, 3, umag013. Fan et al.</a></p>
23 total episodes available
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Frequently asked questions
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- What is Radiology Advances Podcast | RSNA?
- How often does this podcast release new episodes?
This podcast updates daily.
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This podcast is available on 4 platforms including Apple Podcasts, Spotify, and more. You can also use the RSS feed directly.
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