Over the last two decades, large HPC machine efforts have become a procurement exercise.  A large set of applications have been unable to leverage the additional computational power of newly-procured machines without significant additional software development.  The machine architectures need to evolve:  new systems architectures and innovations require a deep understanding of application uses cases and their needs.   Memory and storage, as foundational elements, will be at the center of future innovative systems, driving both greater performance and increased energy efficiency.

• Tackling the issue of underutilized “failed” clinical trial data.
• What lessons can be learned from “failed” clinical trial data and how can these data better inform future clinical studies?

• Automating highly manual processing tasks, translating and digitizing safety case processing and adverse drug reaction documents to make them more usable
• Discuss how optical character recognition (OCR), NLP and deep neural networks are being used to format this data
• Ultimately leading to faster assessment of subject, site and study risks

• Using AI to systematically evaluate the effect of different eligibility criteria on cancer trial populations and outcomes with real-world data
• Ultimately identifying a wider and more accurate pool of patients that could potentially benefit from treatments
• Facilitating the design of more inclusive trials while maintaining safeguards for patient safety

• This session provides the unique opportunity to listen to, and engage with, innovative start-up and middle market companies that are accelerating the integration of AI into clinical trials
• Six companies will take to the stage to deliver quick fire presentations about the work they are carrying out to enhance clinical trials