There should be no question that the world sits on the precipice of a major sea change where artificial intelligence (AI) and machine learning (ML) will be as pervasive as air and infused into our lives, similar to the way the Internet has. AI can analyze information, find anomalies and make informed decisions faster than people can and will be an important tool to help us do our jobs better.
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However, the adoption of AI varies widely by vertical. One of the industries that has embraced AI is healthcare, as it can have lifesaving consequences.
A great example of this is the work that the Boston-based MGH & BWH Center for Clinical Data Science — a collaboration by Mass General Hospital (MGH) and Brigham and Women’s Hospital (BWH) to create, promote and commercialize AI for healthcare — has been doing to accelerate the process of analyzing magnetic resonance images (MRIs).
I recently talked with Neil Tenenholtz, senior machine learning scientist at the MGH & BWH Center for Clinical Data Science (CCDS), to better understand what the organization is doing and how it is leveraging the NVIDIA DGX Station to power their initiatives. (Note: NVIDIA is a client of ZK Research.)
DGX Station brings data center power to the desktop
DGX Station can be thought of as a desktop-size, GPU-enabled super-computer. The primary AI platform from NVIDIA had been the DGX-1, a rack-mountable server that holds eight NVIDIA Tesla V100 GPUs. The challenge with a data center-housed server is that a data scientist would need to work with IT to have the DGX-1 provisioned for that particular task. This usually creates a lag time that could get in the way of the researcher being able to analyze data and conduct deep learning experiments.
DGX Station is a portable, workstation form factor that is powered by 4 Tesla V100 GPUs. Although the form factor is compact, the compute power of a 4 GPU system is the equivalent to approximately 400 x86 CPUs, making DGX Station ideal of computationally intensive processes such as AI, machine learning and data analytics.
AI can improve radiologist efficiency
The mission of the group Tenenholtz works for is to develop AI-based models specifically for radiology and integrate them into the radiologists’ clinical workflow. Successful models can be licensed to external partners or spun out and commercialized through startup, so they may be used by hospitals all over the world. […]
read more – copyright by www.cio.com
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There should be no question that the world sits on the precipice of a major sea change where artificial intelligence (AI) and machine learning (ML) will be as pervasive as air and infused into our lives, similar to the way the Internet has. AI can analyze information, find anomalies and make informed decisions faster than people can and will be an important tool to help us do our jobs better.
copyright by www.cio.com
A great example of this is the work that the Boston-based MGH & BWH Center for Clinical Data Science — a collaboration by Mass General Hospital (MGH) and Brigham and Women’s Hospital (BWH) to create, promote and commercialize AI for healthcare — has been doing to accelerate the process of analyzing magnetic resonance images (MRIs).
I recently talked with Neil Tenenholtz, senior machine learning scientist at the MGH & BWH Center for Clinical Data Science (CCDS), to better understand what the organization is doing and how it is leveraging the NVIDIA DGX Station to power their initiatives. (Note: NVIDIA is a client of ZK Research.)
DGX Station brings data center power to the desktop
DGX Station can be thought of as a desktop-size, GPU-enabled super-computer. The primary AI platform from NVIDIA had been the DGX-1, a rack-mountable server that holds eight NVIDIA Tesla V100 GPUs. The challenge with a data center-housed server is that a data scientist would need to work with IT to have the DGX-1 provisioned for that particular task. This usually creates a lag time that could get in the way of the researcher being able to analyze data and conduct deep learning experiments.
DGX Station is a portable, workstation form factor that is powered by 4 Tesla V100 GPUs. Although the form factor is compact, the compute power of a 4 GPU system is the equivalent to approximately 400 x86 CPUs, making DGX Station ideal of computationally intensive processes such as AI, machine learning and data analytics.
AI can improve radiologist efficiency
The mission of the group Tenenholtz works for is to develop AI-based models specifically for radiology and integrate them into the radiologists’ clinical workflow. Successful models can be licensed to external partners or spun out and commercialized through startup, so they may be used by hospitals all over the world. […]
read more – copyright by www.cio.com
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
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