Dr. Yuh is a neuroradiologist and physicist who interprets images of the brain, neck and spine as attending physician at UCSF and at Zuckerberg San Francisco General Hospital. Recently she was a leader of a team of UC Berkeley computer scientists and UCSF radiologists who developed a deep learning algorithm that identifies tiny abnormalities on clinical head CT scans with accuracy comparable to highly-trained physicians. She also led FDA approval in 2019 of the first-ever imaging biomarker to select patients likely to have poor outcome after traumatic brain injury, for inclusion in therapeutic clinical trials, under the FDA’s Medical Device Development Tool (MDDT) program. A 2002 graduate of Stanford University School of Medicine, she completed training in radiology and neuroradiology at UCSF in 2009. She has been nominated for the UCSF Exceptional Physician Award, and is a prior Outstanding Teaching Fellow of the UCSF Department of Radiology and Biomedical Imaging.
Sharmila Majumdar, UCSF
Matthew Lungren, Stanford
The use of Artificial Intelligence (AI) in diagnostic medical imaging is undergoing extensive evaluation. AI has shown impressive accuracy and sensitivity in the identification of imaging abnormalities and promises to enhance tissue-based detection and characterization. This track will explore technical advancements in clinical machine learning and use cases in radiology.