- Healthcare artificial intelligence (AI) is becoming more prominent as more organizations look to big data analytics to improve healthcare. Machine learning and deep learning solutions continue to grow as entities seek IT infrastructure tools that will help them sort though massive amounts of unstructured data.
Dell EMC announced its new machine learning and deep learning solutions that let organizations use high performance computing (HPC) to process images and support personalized medicine.
The release is a further step toward the company’s goal of using HPC to fully optimize data analytics by using AI technology.
AI is a massive infrastructure undertaking and organizations often don’t know where to start to integrate the technology into their IT environment.
Dell EMC is releasing its AI tool in the form of the Dell EMC Machine and Deep Learning Ready Bundles. The bundles include pre-tested and validated servers, storage, networking and services optimized for machine and deep learning applications.
The bundles aim to help organizations leverage AI capabilities faster with a ready set of tools that have already been tested to work together.
The bundles will include the new Dell EMC PowerEdge C4140 server, supporting NVIDIA Tesla V100 GPU accelerators with PCIe and NVLink high-speed interconnect technology.
GPUs in particular have recently gained popularity in healthcare because of precision medicine and value-based care initiatives. GPUs support the high volume of data required for advanced processing power to handle and analyze the data in real-time.
The Texas Advanced Computing Center (TACC) at the University of Texas at Austin has already used Dell EMC’s technology to conduct research identifying brain tumors. The Center is using machine learning as one of the first applications of its new “Stampede2” supercomputer with Intel Xeon Phi 7250 processors across 4,200 nodes connected with Intel Omni-Path Fabric.
Machine learning and deep learning are not new to the healthcare industry but the rapidly growing technology makes it difficult for many organizations to deploy and manage the systems effectively. The inability to manage AI prevents organizations from using it for real-time analytics at the point of care.
Real-time analytics has the potential to support value-based care initiatives by giving clinicians the data they need to diagnose a patient at the point of care. Diagnosing patients at the point of care with personal analytics results reduces the rate chance of wrong diagnoses and cuts down on the number of return visits, saving entities money.
The Internet of Things (IoT) also plays a large part in the need for more powerful analytics infrastructure. Wearable medical devices also assist organizations looking to cut down on in-person visits for patients with minor health issues or follow up appointments. Physicians are also showing interest in remotely monitoring patients to improve quality of care using wearable medical devices.
A report by Mind Commerce found that IoT devices are critical to collecting data for real-time analytics. Wearable medical devices collect data needed to draw better and more accurate conclusions based on a patient’s personal lifestyle and habits. Patients can even use their own personal wearables such as smartwatches and fitness bands to help healthcare organizations collect this data.
However, the data collected by IoT devices cannot be used effectively if organizations do not have a functioning AI solution to handle, process, and present that data in a way that makes it actionable in real-time.
Although analytic solutions are not the easiest part of a health IT infrastructure to deploy, the growth of medical IoT devices is a promising sign for the future of population health, predictive analytics, and the quality patient care.
Organizations need to consider their future technological goals. If those goals include IoT devices and analytics, entities need to consider how to begin to introduce AI into their health IT infrastructure.