Image Interpretation Radiographer X-ray 
Aidoc
AUGUST 1, 2024
Deep learning, a subset of machine learning, has significantly improved medical imaging analysis. Here are some key advantages: Enhanced Diagnostic Tools Machine learning algorithms improve diagnostic tools by analyzing medical images, such as X-rays and CT scans, to identify patterns indicative of specific disease states.
European Society of Radiology: AI
SEPTEMBER 11, 2023
This study evaluates deep learning (DL) algorithms that are playing an increasingly important role in automatic medical image analysis. The DL algorithm used was trained and externally evaluated on open-source, multi-centre retrospective data that contained radiologist-annotated non-contrast CT head studies.
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AuntMinnie
OCTOBER 2, 2024
tesla MRI AI body composition analysis Cardiac PET Cryo/thermoablation CT colonography Genicular artery embolization Hyperpolarized xenon-129 MRI PET/MRI Photon-counting CT Radiomics Theranostics Whole-body MRI screening Image of the Year 3D PET/MR image. Cognitive Motor Dissociation in Disorders of Consciousness.
AuntMinnie
JANUARY 9, 2025
interpreted around 650 million imaging studies per year. This encompasses 120 million diagnostic X-rays, 91 million CT scans, 42 million MRIs, and 40 million ultrasounds -- numbers that continue to rise at a compound annual growth rate (CAGR) of roughly 4.2% (range 3%-5%, all imaging; 5%-10%, overnight/ER imaging).
Radiology Cafe
FEBRUARY 5, 2019
It all started when Wilhelm Conrad Röntgen discovered X-rays in 1895. After working for weeks in his lab experimenting on the production of ‘strange rays’, which he referred to as ‘X’, he asked his wife Anna Bertha to lend ‘a hand’, the left one to be precise, which he used to produce the first X-ray image.
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