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Most FDA-cleared AI medical devices not tested on patient outcomes
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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.
AI framework could improve MRI quality control in prostate 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
August 18, 2026
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Most FDA-cleared AI medical devices not tested on patient outcomes
By Amerigo Allegretto
Of the 1,059 FDA-cleared devices used in radiology, three had a registered trial.
August 19, 2026
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AI framework could improve MRI quality control in prostate cancer
By Amerigo Allegretto
The framework uses a DL model trained on low- and high-quality MR images and helped improve radiologist accuracy.
August 19, 2026
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.
New immuno-PET tracer shows promise in lung cancer
By Will Morton
Molecular imaging can provide whole-body visualization and quantitative assessment of PD-L1 expression.
August 18, 2026
(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
By Will Morton
No differences in AI performance were observed across centers, and AI assistance was associated with higher reader sensitivity.
August 18, 2026
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.
Radiology Partners unit Mosaic petitions FDA on imaging AI regulation
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The petition seeks clarity on the regulation of AI vision language models used for diagnostic medical image analysis.
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The paper covers risk assessment, pre-market evaluation, and considerations around foundation models and agentic AI systems.
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