Written by Rudeon Snell, Global Partner Lead: Customer Experience & Success at Microsoft
As analytics tools and machine learning capabilities mature, healthcare innovators are speeding up the development of enhanced treatments supported by Azure’s GPU-accelerated AI infrastructure powered by NVIDIA.
Improving diagnosis and elevating patient care
Man’s search for cures and treatments for common ailments has driven millennia of healthcare innovation. From the use of traditional medicine in early history to the rapid medical advances of the past few centuries, healthcare providers are locked in a constant search for effective solutions to old and emerging diseases and conditions.
The pace of healthcare innovation has increased exponentially over the past few decades, with the industry absorbing radical changes as it transitions from a health care to a health cure society. From telemedicine, personalized wellbeing, and precision medicine to genomics and proteomics, all powered by AI and advanced analytics, modern medical researchers can access more supercomputing capabilities than ever before. This quantum leap in computational capability, powered by AI, enables healthcare services dissemination and consumption in ways, and at a pace, that were previously unimaginable.
Today, health and life sciences leaders leverage Microsoft Azure high-performance computing (HPC) and purpose-built AI infrastructure to accelerate insights into genomics, precision medicine, medical imaging, and clinical trials, with virtually no limits to the computing power they have at their disposal. These advanced computing capabilities are allowing healthcare providers to gain deeper insights into medical data by deploying analytics and machine learning tools on top of clinical simulation data, increasing the accuracy of mathematical formulas used for molecular dynamics and enhancing clinical trial simulation.
By utilizing the infrastructure as a service (IaaS) capabilities of Azure HPC and AI, healthcare innovators can overcome the challenges of scale, collaboration, and compliance without adding complexity. And with access to the latest GPU-enabled virtual machines, researchers can fuel innovation through high-end remote visualization, deep learning, and predictive analytics.
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