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NVIDIA Showcases Latest AI-driven Medical Imaging Advancements at RSNA 2019

By MedImaging International staff writers
Posted on 02 Dec 2019
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Image: NVIDIA Clara™ (Photo courtesy of NVIDIA)
Image: NVIDIA Clara™ (Photo courtesy of NVIDIA)
NVIDIA (Santa Clara, CA, USA) demonstrated how GPU-accelerated artificial intelligence (AI) is driving innovation in radiology and transforming the healthcare landscape at the 2019 RSNA Annual Meeting held December 1–6 in Chicago, USA. Visitors to NVIDIA’s RSNA 2019 booth in the event’s AI Showcase saw its latest AI-driven medical imaging advancements and met the company’s deep learning experts about using AI to advance research and accelerate clinical workflows.

The medical imaging industry is being transformed. A decade ago, the earliest applications to take advantage of GPU computing were image & signal processing applications. Today, GPUs are found in almost all imaging modalities, including CT, MRI, X-ray, and ultrasound, bringing more computing capabilities to edge devices. Deep learning research in medical imaging is also booming with more efficient and improved approaches being developed to enable AI-assisted workflows. Today, most of this AI research is being done in isolation and with limited datasets which may lead to overly simplified models. Even when a fully validated model is available, it is a challenge to deploy the algorithm in a local environment.

By equipping the world’s leading institutions with advanced solutions, NVIDIA is enabling them to tackle interoperable data and meet the increasing demand for personalized medicine and next-generation clinics. NVIDIA Clara Medical Imaging is a collection of developer toolkits built on NVIDIA’s compute platform aimed at accelerating compute, AI, and advanced visualization. From automating workflows to improving processing speed and image quality, medical imaging developers are using NVIDIA Clara to harness AI to transform healthcare workloads. With the latest release of Clara AI for Medical Imaging now data scientists, researchers and software developers have the necessary tools, APIs and development framework to train and deploy AI workflows.

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