The sudden emergence of AI models for all sorts of healthcare procedures, along with advanced hardware such as NVIDIA’s GPU processors, creates questions for healthcare practices that want to improve outcomes and costs with these technologies. In a recent video interview, Dan Schneider, Enterprise Platforms Product Specialist at NVIDIA, and Sandra Colner, GM Global Healthcare & Life Sciences at Dell Technologies dove into these important topics and shared some key insights as healthcare leaders navigate AI in their organization.
When used properly, according to Colner, data management and AI-driven diagnostics can “streamline and improve operations.” The term “workflow” turns up a lot in the responses of both interviewees. Thus, Colner says that AI can make workflow decisions and prioritize workflows so they “just run more smoothly.” AI can also provide time-sensitive triage.
The workflow concept is a reminder that administrators and health IT staff have to think at a higher, more connected level instead of just getting individual tasks done. In fact, AI agents can coordinate multiple workflows, “making governance more efficient” in Schneider’s words. Colner calls AI an “end-to-end workflow enabler.”
Colner also says that AI agents become necessary with the growth in both the number and size of images, as well as data sets in general, and the increasing complexity of workflows. Schneider suggests a future use of agents to get the results of a relevant prior scan to the radiologist for comparison purposes.
The option of remote AI processing in the cloud leads to new questions. If a facility wants to subject an image to AI processing or run administrative processes using an AI agent, what should be done locally and what should be done in the cloud? Schneider and Colner point to several considerations, including whether you need instant results (latency) and regulations around protecting patient data (security).
The cloud is often appropriate for analytics. But Schneider points out that sites can’t rely on the cloud always being there, or on adequate bandwidth, especially outside the U.S. Desktop systems are powerful enough for many current AI applications.
How can institutions derive cost benefits by moving AI from pilots into production? Schneider says that it’s an organization issue rather than a technical challenge. The right KPIs must be defined. Ultimately, someone must be responsible for making the shift. He pointed to MONAI (Medical Open Network for AI), an open-source, community-driven framework and ecosystem co-founded by NVIDIA and leading academic medical centers that supports the development, validation, optimization, and deployment of medical AI applications, helping healthcare organizations bring AI from research into production.
Learn more about AI in healthcare from experts from NVIDIA and Dell Technologies in our interview below.
Learn more about NVIDIA: https://www.nvidia.com/en-us/
Learn more about Dell Technologies: https://dell.com/Healthcare
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