Title
Category
Credits
Event date
Cost
  • AMA PRA Category 1 Credit™
  • Nursing
  • Participation
$0.00
This course is designed to assist front-line healthcare facilities with planning for High Consequence Infectious Disease (HCIDs), including Ebola Virus Disease and other special respiratory pathogens, including SARS-CoV-2. The course includes guidance and tools for identifying and isolating patients with Ebola or other HCIDs and informing appropriate stakeholders while providing safe and appropriate care.
  • AMA PRA Category 1 Credit™
  • Participation
11/11/2021
$20.00
DescriptionDespite the growing popularity of deep learning neural networks for various medical imaging applications, the vast majority of algorithms to date represent early proof-of-concept designs that will require a degree of evolution before achieving practical clinical utility. In this talk, we will explore several of these key shortcomings and also discuss new emerging research trends that aim to bridge this gap, including topics such as unsupervised and semi-supervised learning, federated learning and clinical implementation challenges.
  • AMA PRA Category 1 Credit™
  • Participation
11/18/2021
$0.00
DescriptionThe Stepping Strong Center catalyzes multidisciplinary collaborations that inspire groundbreaking innovation, effective prevention, and compassionate intervention to transform care for trauma patients. The Fourth Annual Stepping Strong Symposium features three panel discussions highlighting the continuum of trauma research and care:• Preventing Injuries by Creating Safer Communities for Walkers, Runners, and Bikers• Innovations in Bone and Muscle Healing• Physical, Social, and Emotional Recovery Following Traumatic Injury
  • AMA PRA Category 1 Credit™
  • Participation
12/09/2021
$20.00
DescriptionOver the past 5 years, AI has paved its way into medical imaging.  There has been a plethora of exciting research, multiple international challenges featuring top data-scientists, keen engagement from the engineering and vendor community, resulting in many state-of-the-art AI models. However, there are “nuts and bolts” challenges in implementing AI, and important considerations when it comes to evaluating AI in a real-world clinical environment.