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Use of AI and Synthetic Data in Healthcare
The potential impact of AI in healthcare and medicine looms large. Researchers and clinical practitioners are turning to AI in the search for solutions to many of today’s challenges.

However, bringing reliable AI to the clinic has proven more difficult than initially anticipated. One reason is that modern AI performs best when there is a vast amount of data to learn from.

Data from the medical domain can, by comparison, be small and poorly characterized, making its use for AI training fraught with issues.

In this webinar, we will discuss the impact of training AI with synthetic data and highlight radiology as a successful use case.

Using an AI application developed to automatically analyze chest CT exams, we demonstrate how synthetic data was instrumental in building a new type of application that is in active use across the globe.


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Jason Knapp
Chief Science Officer @Riverain Technologies
Jason leads the research team in the development of advanced AI applications for medical imaging, bringing 20 years of industrial machine-learning experience to bear. Over the past decade, he has developed the methodology used by Riverain for creating AI applications. This includes ensuring the robustness to changes in imaging devices and the use of synthetic disease to train large neural networks. He is the inventor of Riverain’s approaches to bone suppression in chest x-rays and vascular structure in CT scans, two highly successful AI applications. He has been the principal developer of 7 FDA-approved products, which are used worldwide, touching millions of lives each year.
Dr. Anthony Chang
Founder AIMed, Chief Artificial Intelligence Officer @Children’s Hospital of Orange County (CHOC)
I am a pediatric cardiologist and have cared for children with heart disease for the past three decades. In addition, I have an educational background in business and finance as well as healthcare administration and global health - I gained a Masters Degree in Public Health from UCLA and taught Global Health there after I completed the program. Even though I came from a strong academic background and have had renowned mentors as well as have authored and edited many articles and textbooks, I came to realize early in my career that medicine is woefully inadequate and often lacking in data-supported information and knowledge. To better prepare myself for this new era of artificial intelligence, I returned to school and studied biomedical data science and artificial intelligence at Stanford School of Medicine. It was basically a four-year continually epiphanous and intellectually transformational journey.
Dr Robert Gilkeson, BA, MD
Vice Chairman, Research @UH Cleveland Medical Center in Ohio
Dr. Gilkeson is Vice Chairman, Research, UH Cleveland Medical Center in Ohio. He graduated from the Case Western Reserve University School of Medicine in 1989 and did his radiology Residency and Fellowship at Duke University Medical Center. He works in Cleveland, OH and specializes in Cardio-thoracic and Diagnostic Radiology. Dr. Gilkeson spearheaded one of the earliest Lung Cancer Screening programs at UH Cleveland Medical Center and has seen significant increased volume of screening patients over last few years.