Revolutionizing Medical Imaging with AI: Opportunities, Challenges, and Future Outlook 🩺

Artificial intelligence (AI) and machine learning are increasingly being applied to medicine in novel and diverse ways. Rima Arnaout, MD, joins us to demonstrate how machine learning research can be used in cardiovascular imaging. She will also explain how to evaluate new research on machine learning/AI and break down the complexities of convolutional neural networks and how they relate to the larger fields of cardiology and imaging. Speaker: Rima Arnaout, MD is an associate professor of cardiology, a Chan Zuckerberg Biohub investigator, and a faculty member in the Bakar Computational Health Sciences Institute, the Biological and Medical Informatics program, and the Center for Intelligent Imaging at UCSF. She is investigating whether machine learning can be used to detect standard and novel patterns in biomedical imaging in a scalable fashion, with the goals of decreasing diagnostic error in medical imaging and uncovering new phenotypes for precision medicine research. Note: Closed captions will be available within 48-72 hours after posting. Program Bob Wachter: Introduction 00:2:45-00:43:42 – Rima Arnaout, MD, (associate professor of cardiology, Chan Zuckerberg Biohub investigator, faculty at the Bakar Computational Health Sciences Institute, the Biological and Medical Informatics program, and the Center for Intelligent Imaging at UCSF) 00:43:42-01:01 - Q&A with Rima Arnaout and Bob Wachter See previous Medical Grand Rounds: • September 15: A Fireside Chat with Renowned Pandemic Expert Michael Osterholm https://youtu.be/z16SBJNKKds • June 30: America’s Epidemic of Gun Violence: The State of the Science https://www.youtube.com/watch?v=XihYtM8KGF8&t=106s • June 23: Clinical Problem Solving with Sarah Goglin https://www.youtube.com/watch?v=N02tUI5mQX8 • June 16: The Science Behind Real-World Data: Answering Today’s Questions Today https://www.youtube.com/watch?v=wOWtTp11_YQ See all UCSF Covid-19 grand rounds, which have been viewed over 3M times: https://www.youtube.com/playlist?list=PLp2l8GCZXFqSgUfwaResQ7rGwKbGIUi_m