The adoption of AI in hospitals is accelerating rapidly. There are many reasons for this. With Moore’s law broken and computational capability ever increasing, models that save lives and make us more efficient and effective are becoming the norm. Within the next five years, we will see the rise of the “smart hospital,” augmented by workflows incorporating thousands of AI models.
These smart hospitals adopting AI applications face big challenges in IT and infrastructure. Healthcare demands specific restrictions in how data is transmitted, and respecting patient data privacy is paramount. Flexible compute capability, with “write once, run anywhere” capability makes it possible to deploy state-of-the-art applications at the edge in hospitals. Each application demands different compute capabilities for HPC, AI, and visualization.
The NVIDIA Clara Deploy SDK answers this call by providing a reference framework for the deployment of multi-AI, multi-modality workflows in smart hospitals: one architecture orchestrating and scaling imaging, genomics and video processing workloads.
The most pressing problem for deploying AI models is architecting an inference platform that can handle the rapidly changing AI ecosystem, including the increasing number of requests for processing, massive size of healthcare datasets, and diversity of the processing pipelines themselves that use a heterogeneous computing environment.
During GTC Digital 2020, we made available the release candidate for the latest version of the Clara Deploy SDK. It includes platform features and reference applications that enable developers and data scientists with a unified foundation for delivering intelligent workloads and realizing the vision of the smart hospital. Figure 2 shows the Clara Deploy SDK technology stack.
Read the full blog, Deploying Healthcare AI Workflows with the NVIDIA Clara Deploy Application Framework, on the NVIDIA Developer Blog.