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Most FDA-cleared AI medical devices not tested on patient outcomes
Of the 1,059 FDA-cleared devices used in radiology, three had a registered trial.
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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.
MRI
AI framework could improve MRI quality control in prostate cancer
(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%).
Molecular Imaging
New immuno-PET tracer shows promise in lung cancer
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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.
AI fracture detection device validated across three European hospitals
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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.
MRI
AI framework could improve MRI quality control in prostate cancer
Molecular Imaging
New immuno-PET tracer shows promise in lung cancer
Digital X-Ray
AI fracture detection device validated across three European hospitals
CT
CT for breast radiotherapy detects ischemic heart disease risk
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Artificial Intelligence Ai Global Communication Network 400
AI
Most FDA-cleared AI medical devices not tested on patient outcomes
Womens Imaging
Longitudinal DBT could help improve long-term cancer risk prediction
AI
AI tools may not meet breast radiologists' expectations in practice
AI
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A Ibrain
Molecular Imaging
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Practice Management
Rsna
RSNA R&E Foundation awards $4.5M in grants in 2026
The grants fund 79 projects supporting medical imaging research and education.
Most FDA-cleared AI medical devices not tested on patient outcomes
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