The NVIDIA GPU-accelarated cloud gives healthcare organizations on-site access to the latest deep learning frameworks, providing the performance and flexibility to take on challenging AI projects.

These deep learning containers can run in the cloud with Alibaba, Amazon, Google, Microsoft, and Oracle.

We spoke to Craig Rhodes, Industry Business Development for AI in Pharma, Healthcare and Life Sciences at NVIDIA to learn more about the competitive cloud space.

Nvidia fact-file

Established by: Jensen Huang

Founded: 1993

Headquarters: Santa Clara, CA

  • What trends are you currently seeing emerging in the space of cloud computing and AI that you find exciting?

If you look two or three years ago, there was a huge nervousness about moving clinical data or doing AI and analytics in the cloud. I think nowadays it’s becoming more commonly accepted that some of your workloads will be shared with an on-premise and cloud-based solution.

Everybody knows they need to put cloud into their programme and I think what they’re now trying to work out is: which data types can be put into the cloud; the correct governance and regulation and which cloud providers will provide the best security framework to do that.

  • How does NVIDIA collaborate with healthcare providers to drive innovation and results in clinical settings?

NVIDIA Inception programmes are an excellent way to get involved with us and learn about what we’re doing and how we’re doing it.

We share all of our cutting edge and ground-breaking inception partners which might be relevant to your organization.

  • What is most important to know when brining these tools into clinical settings?

Governance and regulatory demands of the data, how to apply those to the clinical data and making sure that your solution has considered those, because there are many different types of rules depending on which country you’re in and which type of data you’re using.

  • Why did NVIDIA move into the healthcare space?

The GPU, which accelerates graphics and processing of those graphics, improving resolutions, lends itself so nicely to analysis of medical imaging that it was just a natural step.

  • Where do you see the space moving in the short term (2-3 years) and in the long term (9-10 years) and what will be the main driver?

I think the big thing is to end the silos of data and enable us to use and analyze different data types (pathology, genomics, clinical data) all together. I think that’s a fundamental change that needs to happen in the future.

In the long term I would see the ability for a clinician to have much more information about a patient, to be able to possibly sequence that patient, to be able to get the results very quickly and then be able to determine the best prescription or diagnosis for that patient. That’s a very big change, which could take 10 or 20 years.

Interview originally appeared in AIMed Magazine issue 04, available online here.

 Photo credit: BagoGames

nvidia artificial intelligence healthcare medicine cloudCraig Rhodes, Industry Business Development for AI in Pharma, Healthcare and Life Sciences at NVIDIA

Craig Rhodes is an experienced Innovation Manager with a demonstrated history of working in the Healthcare and Life Sciences industry. Skilled in IT Strategy, Management, Product Design, Development and Management. Managing Tier 1 in Health and Life Sciences customer requirements and deliverables ensuring successful requirements understanding and so solution delivery.

He engages with strategic partners and customers to accelerate artificial intelligence within the Pharmaceutical, Biotechnology and Healthcare industry within Europe, Middle East and Africa.