HIGHLIGHTS
SUMMARY
Radiology is at the forefront of applied artificial_intelligence (AI) due to the digitization and archiving of vast numbers of radiology images coupled with the availability of highperformance, low-cost computers. In the context of imaging, by applying an ML algorithm to images (such as CT, MRI, or FDG-PET images) and given some prior knowledge about these images (such as whether they contain a benign or malignant tumor), the algorithm can learn from training images and apply this knowledge to unseen images to make a prediction. Training refers to the ML model learning from . . .
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