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The hype around artificial intelligence (AI) in medical imaging has led to plenty of discussions of its impact in clinical and academic spaces. To explore current and future implementations of AI in medical imaging at academic institutions, Health Imaging spoke with Curtis Langlotz, PhD, Stanford University’s Medical Informatics Director for Radiology. Langlotz discusses the implementation of AI and machine learning in clinical imaging as this year’s Dwyer Lecturer at the SIIM 2018 Annual Meeting from May 31 to June 2 in National Harbor, Maryland.  
Feb 14 2018 | Deep Learning in Neuroradiology | Posted In: News
SUMMARY: Deep learning is a form of machine learning using a convolutional neural network architecture that shows tremendous promise for imaging applications. It is increasingly being adapted from its original demonstration in computer vision applications to medical imaging. Because of the high volume and wealth of multimodal imaging information acquired in typical studies, neuroradiology is poised to be an early adopter of deep learning. Compelling deep learning research applications have been demonstrated, and their use is likely to grow rapidly.
Dr. Curtis Langlotz speaking to the National Academy of Medicine's National Cancer Policy Forum on how artificial intelligence will change imaging diagnosis of cancer  
Jan 12 2018 | Posted In: News
In PET imaging, the amount of radiotracer dose typically correlates with the level of image quality.  But researchers from Stanford University have trained a deep-learning algorithm to process ultralow-dose PET image data and then create synthetic images that approximate PET scans acquired using a standard radiotracer dose.