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HEALTHCARE

Dell, Intel Collaboration Advances Life Science Technology

Two scientists looking over data on a computer screen in biomedical research.

AI in life sciences technology is nothing new. It’s widely used in fields like genomics, drug discovery, and personalized medicine. But now AI and machine learning are accelerating innovation across almost every aspect of the industry—in ways we could not have imagined.

Take, for example, the short development cycles and ability to respond to new variants of mRNA-based Covid-19 vaccines. With millions of lives saved, it’s clear that AI-powered data analytics in medical research and development has an exceptionally positive impact on society.

As AI takes over a huge chunk of workloads, we see an exponential growth of medical research technology data, which is being processed, analyzed, shared, and secured—from the network edge to the core cloud. But the computing infrastructure required to power these massive workloads is complex. And while domain experts like research scientists and computational biologists don’t want to worry about what lies beneath their data, IT teams must.

Mindy Cancila, Vice President, Strategic Business Development of Dell Technologies, explains in a recent blog post: “The complexity of managing diverse workloads and data across a variety of environments has become daunting as organizations scale their efforts. As IT decision makers consider new hardware, software, and devices today, they’re less concerned with product specs, speeds, and feeds, and more with how these investments will drive ROI and deliver true business outcomes.”

Recognizing this shift, Intel and Dell have partnered to simplify deployment of edge-to-cloud hardware, software, and tools—empowering life sciences organizations to stay ahead of the curve and achieve their goals with greater efficiency and confidence.

“We’re enabling innovation with scalable performance, unwavering reliability, comprehensive support, and future-ready infrastructure,” says Alex Long, Head of Life Sciences Sales Strategy at Dell. “For example, I work with pharma companies deploying new instrumentation for genetics research. We help them plan for how to transition from handling gigabytes of data in a month to petabytes in a week.”

“We’re enabling #innovation with scalable performance, unwavering reliability, comprehensive support, and future-ready infrastructure.” – Alex Long, @DellTech via @insightdottech

Rightsized Life Science Technology Infrastructure

But as infrastructure demands grow, IT organizations continually face doing more on a budget without sacrificing pace of innovation. They are not just managing rapid growth of data creation and sharing but also the computing infrastructure to support it. 

Intel and Dell deliver rightsized technology designed for life sciences use cases that scales for the future without huge investments in new hardware and software.

One example of how organizations can manage costs is re-evaluating their assumption that heavy AI workflows must be developed and run on expensive GPUs. In the vast majority of use cases, Intel processor-powered Dell systems have computational capacity that reduces the need for GPUs both at the edge and in the data center.

“About 80% of the customers I’ve talked to have oversized the volume of GPUs they need and the processing power they’re actually going to use,” says Long. “There are a lot of workflows that should go directly to the CPU, which translates to lower CapEx and OpEx.”

Open Source: Fundamental to Healthcare Solution Development

Another challenge that GPUs can introduce is future vendor lock-in. Leveraging open source software like PyTorch and TensorFlow prevents that problem while also controlling development costs.

And it’s not just cost. The life sciences R&D community has a long history in using open source software, which plays a role in code validation, collaboration, flexibility, and investment protection.

Intel’s commitment to open source includes software tools and hardware optimizations that enhance the use of PyTorch, TensorFlow, and other software across its product line.

As innovation accelerates, life sciences R&D, clinical trials, and more become increasingly collaborative—achieving new advancements faster. This drives not just rapid growth in AI-generated data, but also an increase in federated learning across different domains, causing a massive need to secure data in new ways.

For example, sharing research data must be done in a confidential way. Dell and Intel take a layered approach to protecting data at the edge and in the cloud. Hardware- and software-enhanced technology, like Intel® Threat Detection Technology, Intel® Software Guard Extensions, and Intel® QuickAssist Technology, delivers a foundation to secure data on and off premises.

The Future of Medical Research Technology

Scientists, developers, and IT will continue to face complex infrastructure requirements well into the future. “Focus on the domain,” says Long. “With combined decades of experience in life sciences, you can leave much of the system design to Intel and Dell.”

This gives computation biologists freedom to focus on their approach in gaining the most from AI and to handle the onslaught of data to maximum advantage. Even with expertise in AI-driven analytics, they must consider if and how to retrain models, leverage existing ones, or build something brand new.

“When you start working with these organizations, it becomes very clear that we’re just scratching the surface of what they need,” says Long. “When you talk to them about how they’re approaching this, that’s the ultimate end story. The Dell and Intel partnership offers computing platforms and infrastructure the life sciences segment requires to handle a massive increase in data workloads—from the edge to the data center.”

 

This article was edited by Christina Cardoza, Editorial Director for insight.tech.

About the Author

Georganne Benesch is an Editorial Director for insight.tech. Before this she was an independent writer, authoring blogs, web content, solution guides, white papers and more. Prior to her freelance career Georganne held product management and marketing positions at companies such as Cisco, Proxim and Netopia. She earned a B.A. at University of California at Santa Cruz.

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