Artificial Intelligence

Artificial intelligence (AI) is becoming a crucial component of healthcare to help augment physicians and make them more efficient. In medical imaging, it is helping radiologists more efficiently manage PACS worklists, enable structured reporting, auto detect injuries and diseases, and to pull in relevant prior exams and patient data. In cardiology, AI is helping automate tasks and measurements on imaging and in reporting systems, guides novice echo users to improve imaging and accuracy, and can risk stratify patients. AI includes deep learning algorithms, machine learning, computer-aided detection (CAD) systems, and convolutional neural networks. 

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AI can help radiologists ID difficult pneumothorax cases

"The AI we use works almost like magic—and it will help radiologists save lives," said Antonio Sze-To, a postdoctoral fellow who worked on the project.

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Microsoft, Nuance using AI to ‘power the exam room of the future’

Microsoft and Nuance Communications announced Thursday, Oct. 17, a collaboration on ambient clinical intelligence (ACI) solutions designed for healthcare providers.

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UCSF launches ‘Intelligent Imaging Hub,’ targets opioid addiction with grant money

UCSF’s Center for Intelligent Imaging will partner with Santa Clara, California-based NVIDIA to help build AI tools that can be used in everyday practice.

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King’s College London, NVIDIA launch federated learning system for neural networks

Santa Clara, California-based NVIDIA and King's College London are teaming up to create a new federated learning system to advance medical imaging research.

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Silicon Valley tech company gains FDA approval for AI-powered image cleanup tool

SubtleMR, as the product is called, is an image-processing software that deploys deep-learning algorithms to bolster images created by any scanner. 

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MRI scans connect head injury microbleeds to poor outcomes

Images of patients with traumatic head injuries revealed that microbleeds appear in the form of small, dark lesions and may predict worse outcomes, according to a new study published in Brain.

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NVIDIA, King’s College London develop federated learning system for medical imaging

Researchers from NVIDIA and King’s College London have collaborated on a new federated learning system specifically designed for the interpretation of medical images.

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CT model offers ventilation insights into ‘relatively unknown’ lung regions

Researchers found measurements performed with their full-scale airway network flow model based on CT imaging data compared similarly to measurements derived from functional lung imaging. In addition to improving COPD analysis, the platform can help shed light on many forms of lung disease.