Artificial Intelligence for Medical Imaging

Track 1

Tuesday, April 26, 2022 - 1:00 pm to 1:30 pm

The ever-growing use of medical imaging means that some physicians are now evaluating many thousands of images per typical workday, outstripping human ability to thoroughly evaluate each image. AI could be an answer, but physicians train for years to acquire the expertise to read these images. This talk describes challenges and solutions in the development of an AI, through close collaboration of UC Berkeley computer scientists with doctors at UCSF, that rivals experts at interpretation of head "CAT scans," a common medical imaging study with over 20 million performed each year in the U.S. alone.

Esther Yuh, MD

Professor in Residence

University of California, San Francisco

Esther Yuh, M.D., Ph.D., is professor of radiology and biomedical imaging. She obtained her M.D. at Stanford University in 2002, and completed an internship in Internal Medicine at Stanford University Hospitals and Clinics in 2007, Radiology residency at UCSF in 2007, and advanced fellowship training in Neuroradiology at UCSF in 2009. She is a specialist in using imaging technologies to diagnose and treat disorders of the brain, spine, neck, and other parts of the central and peripheral nervous systems. Her expertise includes image-guided spinal treatments and computer-aided detection of head trauma and other neurological disorders.

Yuh's honors include the General Electric – Association of University Radiologists Research Academic Fellowship, UCSF Department of Radiology Outstanding Fellow Teaching Award and a nomination for the UCSF Medical Center Exceptional Physician Award.