Healthcare is a complex industry with many challenges, but cloud-based infrastructure that is optimized for AI has emerged as a key foundation for innovation and operationalization. Combined with high-performance computing (HPC), cloud-based infrastructure can provide the elasticity, flexibility, and simplicity needed for healthcare organizations to manage workloads of any size.

Silvain Beriault, AI strategy lead and lead research scientist at Elekta, a global innovator of precision radiotherapy systems for cancer treatment, and John K. Lee, AI platform and infrastructure principal lead at Microsoft Azure, recently discussed how cloud-based AI infrastructure has driven improved collaboration and innovation for Elekta’s worldwide R&D efforts aimed at improving and expanding the company’s brain imaging and MR-guided radiotherapy across the globe.

Lee explains that cloud-based infrastructure-as-a-service (IaaS) for AI provides significant benefits, including the ability to begin with a proof of concept (PoC) and scale up or out as needed. Cloud-based infrastructure services also deliver faster time-to-value and better total cost of ownership and return on investment than building on-premises AI architecture from scratch.

Elekta is a medical technology company developing image-guided clinical solutions for the management of brain disorders and improved cancer care. When the COVID pandemic forced researchers out of their labs, company leaders saw an opportunity to accelerate and expand efforts to shift AI R&D to the cloud which had begun a few years earlier. Elekta’s AI head knew a more robust, accessible cloud-based architecture would help Elekta advance its mission of increasing access to healthcare.

Beriault notes that the cost analysis was also a crucial factor in the decision to adopt cloud-based infrastructure. It would be difficult to estimate current and future needs in terms of high-performance computing, and the cost of maintaining on-prem infrastructure for AI and its limitations extend far beyond purchasing GPUs and servers.

Elekta turned to Azure ML for their infrastructure, as well as crucial support as teams learned to use the platform portal and APIs to begin launching jobs in the cloud. Microsoft worked with the team to build a data infrastructure very specific to the company’s domain and dealt with crucial data security and privacy issues.

Beriault says adopting cloud-based architecture lets researchers focus on their work and develop the best possible AI models instead of building and “babysitting” AI infrastructure. Lee comments that choosing a partner who can provide that kind of service is crucial, and Microsoft’s collaboration with NVIDIA to develop foundations for enterprise AI is critical for customers like Elekta.


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