Bringing artificial intelligence healthcare solutions to life

Healthcare organizations face many challenges due to increasing population and urbanization, longer life spans and increasingly sedentary lifestyles, not to mention long-standing battles with communicable diseases. These challenges are requiring healthcare providers to significantly change the way they operate. They must focus on delivering medicine that is tailored for each patient while also deepening their understanding of population health to better respond to patterns.

WWT has been working with healthcare organizations for decades to create technology solutions that improve care and outcomes. At WWT’s Advanced Technology Center, our best-of-breed technology partners can help deliver exceptional results through artificial intelligence. Intel® processors, FPGAs and optimized frameworks are the foundation for edge-to-cloud solutions that accelerate workloads at each critical point.

We can work with you to meet your objectives using the artificial intelligence and advanced analytics that are possible today.

WWT Focus Areas for AI in Healthcare

Operational

  • Optimize hospital staffing
  • Improve patient flows
  • Reduce paperwork/billing inefficiencies
  • Reduce unnecessary tests and procedures

Clinical

Financial

  • Optimize payment plans
  • Streamline claims processing
  • Image Classification of Race Cars

    In this paper we explore the use of artificial intelligence to perform an image sorting task to gain a competitive edge in a NASCAR race.
  • Recommendation Engine: ATC Connect

    A hybrid recommendation engine was built for recommending relevant articles to the users of the WWT ATC Connect mobile application.
  • Deep Learning with NVIDIA DGX-1

    We have highlighted some practical considerations for the Deep Learning practitioner relevant to neural network training on the NVIDIA DGX-1. Benchmarking experiments showed that GPU performance is related to three dimensions.
  • Machine Learning for Shovel Tooth Failure Detection

    Current computer vision-based methods for identifying broken teeth on mining shovels suffer from a prohibitively high false positive rate (FPR). We describe a two-stage methodology for the detection of broken teeth that reduces the FPR.