Nvidia’s Work in Healthcare Is Just Getting Started
Nvidia’s meteoric success has made global headlines in the last few months. One of the primary reasons for this is the rapid growth and skyrocketing demand for the hardware to support advanced artificial intelligence systems—a space that Nvidia has been innovating in for decades.
In congruence with this innovation, Nvidia’s work has helped enable significant progress across numerous industries, and healthcare has been at the forefront of benefitting from this progress. In fact, Nvidia’s healthcare focused work has been ongoing for nearly two decades, especially as the company slowly started transitioning from a traditional graphics card company to focus on advanced computing and hardware. Although not a traditional healthcare company itself, Nvidia has played a pivotal role in the massive changes that the healthcare industry has endured over the last few decades, transitioning from paper records and clipboards to digital records and imaging.
The company has especially focused on the explosion of healthcare data and harnessing this data to gather longitudinal insights, collating it for more advanced applications, and using it to build advanced models to power useful and tangible applications for end-users to actually help drive better outcomes. With regards to this, Kimberly Powell, vice president and general manager of healthcare at Nvidia, explains that the company has focused on a variety of key application areas, including digital surgery, digital biology and digital health.
The umbrella of digital surgery is best explained by the company’s revolutionary work with Holoscan, which is a sensor processing platform that empowers the “software and hardware needed to build AI applications and deploy sensor processing capabilities from edge to cloud.” As the company describes edge computing, “At the edge, IoT and mobile devices use embedded processors to collect data. Edge computing takes the power of AI directly to those devices and processes the captured data at its source—instead of in the cloud or data center.” Using this technology, innovators can develop and deploy medical devices that can bring AI applications and advanced models directly to the patient care setting and operating room. Leveraging multimodal sensors and advanced models, this can aid clinicians in making decisions in real-time.
Similarly, digital biology is a vast ecosystem and encompasses Nvidia’s work with instrumentation companies across a variety of applications including genomics, microscopy, DNA sequencing, computational biology and many more to enable a more insight-driven approach to life sciences and drug discovery. This field is notorious for the incredibly vast amount of data that is often collected and analyzed as a part of the scientific process; the value of Nvidia’s technology is that it empowers the training of foundational models at scale with incredibly high levels of computing power, which enables researchers to create complex models and replicate them to ingest these large amounts of data.
Finally, the rise of digital health has also brought with it the potential for incredible applications in clinical settings. With a variety of artificial intelligence models being deployed in this space, clinicians can now leverage various tools to ease their workflows and augment their day-to-day processes, including the use of enterprise search applications across data sets to employing intelligent AI assistants, which can help document and summarize physician-patient interactions and ease administrative burdens.
Although these three generic categories do not necessarily capture the entirety of the work that Nvidia is doing in the healthcare space, they provide a sample of the numerous applications that have emerged from the company’s progress. Undoubtedly, this market is incredibly competitive, as more innovators are rapidly entering this space. Market behemoths in the healthcare technology and AI space include large technology companies such as Google in addition to the hundreds of smaller startups that are attempting to disrupt this industry.
Despite the incredible progress, however, Powell thoughtfully explains that we have to remain cautious, and that this work has to be approached in a calibrated manner: “Our job is to create the tools and the frameworks to help build and scale the models; we are still learning how to build the models and deploy this technology safely.” Indeed, this learning process will never truly end, especially as the technology rapidly evolves. Nevertheless, if developed in a safe and sustainable manner, this area of innovation has the potential to truly make an impact in healthcare.
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