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Artificial Intelligence Homepage

BSWH to install Glassbeam's CLEAN blueprint to leverage machine uptime Will include integrated CMMS software by EQ2

Machine learning reduces false positives for lung cancer in low-dose CT False positives occur at rate of 96 percent

Beyond the hype: How practical AI is enhancing radiology Insights from Imad B. Nijim, chief information officer for MEDNAX Radiology Solutions

Siemens and ESR present first Digital Experience Hall at ECR Aims to stimulate exchange of thoughts and knowledge about digitization in radiology

Canon showcases CT image reconstruction tech and software upgrade at ECR Removing noise while preserving signals

GE debuts work-in-progress algorithms at ECR 2019 For predicting no-shows and detecting the presence of pneumothoraces

Personalized cardiac test could eliminate unnecessary catheterizations Examines flow of blood with AI and CT

Women's brains appear three years younger than men's at the same age: PET study A machine-learning algorithm assisted with the analysis

AI and personalized medicine to raise the bar in radiology reporting New innovations could provide myriad benefits to clinical workflow

Hologic launches Unifi Analytics to curb mammo downtime Predicting tube failures before they happen and setting performance benchmarks

NVIDIA, Scripps partner on AI for genomics, sensors

by Thomas Dworetzky , Contributing Reporter
Scripps Research Translational Institute and NVIDIA are teaming up to push the frontiers of AI and deep learning into both genomics and sensor technology.

They will set up a center focusing on applications in those fields, the company announced on its blog Tuesday.

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AI in medicine has tremendous promise, but a lot depends on validating its algorithms – and data sensors are key to “proving clinical efficacy,” said the institute's founder, Eric Topol, adding, “the data inputs from sensors and sequencing, in particular, will play an important role.”

The current efforts will focus on a deep-learning approach to vast data sets of health information – now doubling every seven months – thanks to faster, cheaper sequencing equipment, and the growth of wearables and personal sensors like smartwatches, blood pressure cuffs and glucose monitors. The growth is so fast, in fact, that deep-learning approaches have increased their use in genomic research papers 40 times in just the last four years, according to NVIDIA.

NVIDIA has made other deals to further health-oriented research recently. Earlier in October, it partnered with King’s College London enabling researchers to deploy NVIDIA's DGX-2 IA research system and the NVIDIA Clara platform.

Kimberly Powell, vice president of healthcare at NVIDIA, said the DGX-2, with a large memory and two petaflops of computing power, allows for faster training of massive 3D volumetric data sets and enhances the use of Niftynet, which trains neural networks for medical imaging.

“It would, historically, take many weeks to do one training cycle,” Powell told HCB News. “With the DGX-2, you can cut that down to a couple of hours.”

In August, Vyasa Analytics, a deep learning software maker in the healthcare field,
For example, the software is able to integrate and analyze this complicated data to address questions in areas including business development, EHR analytics, compliance/fraud detection, crystal morphology classification for formulation, drug repurposing and de novo compound design, said the company.
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