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.
Developing Expert-Level AI For Imaging In Brain Emergencies
Continued increases in medical imaging mean physicians now evaluate thousands of images per typical workday. AI could be an answer, but physicians train for years to gain expertise to read these images. This talk describes the development of an AI to rival experts at interpreting head CT, a common study performed worldwide.
In order to expedite clinical diagnostics and advance precision patient care, innovative developments in algorithm development and imaging sciences, combined with improved understanding of the complex biology of cancer is crucial. This session will cover various developments, needs, and opportunities of expedited clinical decision-making.