Co-Founder, CEO Oatmeal Health. Dr. Vachon is a practicing radiologist, entrepreneur, speaker and author on machine learning in medical imaging and healthcare.He is determined to provide clinically useful tools for his healthcare colleagues to best leverage the vast amounts of data generated daily. He is a 16 year Navy veteran and lives in San Diego California.
Dr. Li is a global leader conducting multi-disciplinary research to enable artificial intelligence (AI) in clinical imaging. He is an associate professor at Case Western Reserve University. Before that, he was an associate professor in the Western University and a scientist at the Lawson Health Research Institute. He was a scientist at GE Healthcare (2006-2015). He is a committee member in multiple highly influential conferences and societies. He is most notable for serving on the prestigious board of directors in the MICCAI society (2015-2024), where he is also the general chair for the MICCAI 2022 conference, which is the most influential AI-in-imaging conference. He has over 200 publications, acted as the editor for six Springer books, and serves as an associate editor for several prestigious journals in the field. Throughout his career, he has received several awards from GE, various institutes, and international organizations.
Theme: Pragmatic AI applications in Radiology
– “Practical AI in Lung Cancer Screening“: Dr. Ty Vachon, Co-Founder, CEO Oatmeal Health.
In his talk, Dr. Vachon addresses the use of AI in series to prepare for Lung Cancer Screening HEDIS Measures EHR tool to find patients, imaging tool to evaluate and nudging tool to follow up.
– “AI to eliminate chemical contrast agent in imaging“: Dr. Shuo Li, Associate professor at Case Western Reserve University.
Chemical contrast agent-enhanced imaging plays a critical role in clinical diagnostic imaging. Recently there has been an increasing concern regarding the chemical contrast agent used in the clinic. In this talk, Dr. Li will share his ground-breaking AI contrast-enhanced imaging, which develops state-of-the-art machine-learning techniques to eliminate the need for chemical contrast agents.