Kimberly Powell, who leads Nvidia's efforts in health care, says the company is working with medical researchers in a range of areas and will look to expand these efforts in coming years. Most notably, a machine-learning technique called deep learning is being applied to processing medical images and sifting through large amounts of medical data. Nvidia is, for example, working with Bradley Erickson, a neuro-radiologist at the Mayo Clinic, to apply deep learning to brain images. There are, however, significant challenges in applying techniques like deep learning to medicine.
Jensen Huang, the billionaire CEO of Nvidia, has made a fortune by supplying the hardware used for artificial-intelligence algorithms. He's now betting that AI is about to become an indispensable part of medicine. In the early 1990s, Huang recognized that the limitations of general-purpose computer chips and the rise of computer gaming would be likely to increase demand for specialized graphics processors. During the late '90s and 2000s, the company he cofounded found huge success making high-end graphics chips for gamers. More recently Huang and Nvidia have ridden a different technology wave, supplying the hardware used to train and run the deep-learning algorithms that have been key to a recent renaissance in artificial intelligence.
As medical imaging technology continues to take advantage of every new deep learning breakthrough, the challenge is that the computing technology on which it relies must evolve just as quickly. A company called Nvidia is leading that charge under the guidance of Kimberley Powell, who is confident that Nvidia's processors are not only meeting the deep learning standards of medical imagining, but also pushing the industry forward as a whole. Nvidia's hardware has established its silent but prominent role in deep learning's marriage with medicine. Powell believes projects like their specialized computers, such as the DGX-1 a powerful deep-learning product, will become increasingly more common in hospitals and medical research centers. Strong computing power, like what the DGX-1 can provide, stands to increase the reliability of the diagnostic process; something that, in turn, would significantly boost the standard of care in developing countries.
Nvidia and the Scripps Research Translational Institute announced Tuesday that they're partnering to advance the use of artificial intelligence for early disease prediction and prevention. More specifically, they'll establish a center of excellence to accelerate the creation of AI applications that use genomic and digital health sensor data. So far, AI applications in medicine have largely focused on medical imaging, Kimberly Powell, vice president of healthcare at Nvidia, told reporters last week. While medical imaging is a powerful diagnostic tool, she said that AI needs to be applied to the medical data being collected from a growing number of sources. Data from DNA profiles or wearable technology, for instance, can go beyond diagnostics to help medical practitioners and researchers "think about the prevention of disease or the prediction of risk of disease in the first place."
Nvidia unveiled a new federated learning edge computing reference application for radiology to help hospitals crunch medical data for better disease detection while protecting patient privacy. Called Clara Federal Learning, the system relies on Nvidia EGX, a computing platform which was announced earlier in 2019. It uses the Jetson Nano low wattage computer which can provide up to one-half trillion operations per second of processing for tasks like image recognition. EGX allows low-latency artificial intelligence at the edge to act on data, in this case images from MRIs, CT scans and more. Nvidia made its announcement of Clara on Sunday at the Radiological Society of North America conference in Chicago.