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Ultrasound MinnieCast, Episode 15: Wearable ultrasound making strides
Geonho (Tom) Park, PhD, from Stanford Medicine shares insights from the work he and colleagues have done in wearable ultrasound.
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Thermal ablation achieves long-term survival in kidney cancer
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Most FDA-cleared AI medical devices not tested on patient outcomes
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What independent clinics need to know before buying a CT scanner
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Nominations Are Open — 2026 Minnies
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Nominations Are Open — 2026 Minnies
August 6, 2026
Food Insecurity
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Check out our Cases of the Week!
80-year-old man with altered mental status, seizure revisited
An 80-year-old man with altered mental status and new-onset seizure one week after a fall.
20-year-old man with testicular mass revisited
38-year-old man with episode of sudden unresponsiveness revisited
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What independent clinics need to know before buying a CT scanner
Radiation Oncology/Therapy
Radiotherapy access could address gynecological cancer disparities
Radiation Oncology/Therapy
Food insecurity linked to higher risk of death in breast cancer
MRI
How much surveillance imaging is needed for meningiomas?
Ultrasound
Ultrasound MinnieCast, Episode 15: Wearable ultrasound making strides
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Most FDA-cleared AI medical devices not tested on patient outcomes
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Longitudinal DBT could help improve long-term cancer risk prediction
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AI tools may not meet breast radiologists' expectations in practice
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Molecu Light
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Interventional
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RSNA R&E Foundation awards $4.5M in grants in 2026
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Florida approves NBPAS as recognized board certification option
NBPAS-certified physicians can now represent themselves as board certified in Florida physician advertising.
RSNA R&E Foundation awards $4.5M in grants in 2026
Most FDA-cleared AI medical devices not tested on patient outcomes
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PET/CT outperforms MRI after focal therapy for prostate cancer
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True-positive PSMA-PET with positive MRI in 74-year-old male with prostate cancer (Gleason score of 4 + 5; left anterior and posterior apex positive cores). (A) Left posterior peripheral apical to midprostate hyposignal visible on transaxial MRI. (B) No definite CT correlate evident (white arrow). (C) Focal high PSMA expression detectable in left posterior mid to base with SUVmax of 12.4 on transaxial fused delayed pelvic PET/CT (yellow arrow). (D) Focal high PSMA expression found in left midgland to base of prostate on maximum-intensity-projection image (yellow arrow).
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Low-quality axial T2-weighted MRI scans with gradient-weighted class activation mapping. The low-quality MRI scans (top row) show blurred structures and artifacts making zonal and capsule boundaries indistinguishable. On the same scans with gradient-weighted class activation map overlays (bottom row), red indicates regions that were most heavily weighted by the deep learning model toward the low-quality prediction.
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Molecular imaging can provide whole-body visualization and quantitative assessment of PD-L1 expression.
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(A) Ga-68 NK224 PET/CT maximum intensity projection image (left), axial PET/CT images (center), and axial CT images (right) in a 43-year-old female participant with lung adenocarcinoma show lesions in the right cerebellum (blue arrow), right frontal lobe (red arrow), and sternum (green arrow) with low, intermediate, and high tracer uptake, respectively. Photomicrographs (scale bar, 100 μm) with hematoxylin-eosin (H&E) staining (center bottom) and programmed death ligand 1 (PD-L1) immunohistochemical (IHC) staining (right bottom) show high PD-L1 expression in the sternal lesion (tumor proportion score ≥ 50%).
AI fracture detection device validated across three European hospitals
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Artificial intelligence (AI) assistance facilitates correct fracture identification in cases initially missed by human readers. A representative case is shown using anteroposterior radiographs in a 29-year-old male patient with wrist trauma (suspected nondisplaced transverse scaphoid fracture). The reader’s initial unaided assessment (top) resulted in a missed fracture, whereas with AI assistance (bottom), the fracture was correctly identified. The green box represents the fracture location as predicted by the AI tool, the blue box represents the reference standard annotation, and the red dots represent individual reader annotations.