Rajesh Shah, an Indian American professor of clinical radiology and Director of Clinical Trials at University of California San Francisco (UCSF), has become the Associate Chair for Quality and Safety.
This high-visibility role within the department and Health system ensures a strategic approach to assessing and standardizing high quality, safe imaging for UCSF patients, according to a university release.
Dr. Shah joined the UCSF faculty in 2024 from California Pacific Medical Center, where he served as director of interventional radiology. Before that, he was interim chief of radiology at the VA Palo Health Care Systems and associate professors of radiology at Stanford University.
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For a decade, he has served on Society for Interventional Radiology (SIR) quality and safety committees, creating the Quality and Performance Improvement Division and serving as its Division Councilor for the past seven years.
In his short time at UCSF, Dr. Shah has exemplified the ‘phenotype’ of an academic radiologist. He established a genicular artery embolization and genicular nerve ablation program, expanded a lung ablation program, and launched a histotripsy program with IR colleagues Nicholas Fidelman, and R. Peter Lokken.
His research applies foundation AI models to text within the electronic health record to detect procedure related complications and medical device-related issues.
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As associate chair, Dr. Shah oversees departmental and UCSF imaging safety policies and will chair the department’s Quality and Safety committee. He will represent the department on the Health system’s Clinical Performance and Medical Director committees and work closely with the UCSF Health Chief Quality Officer.
Dr. Shah earned his medical degree at The University of Chicago Pritzker School of Medicine in 2004, followed by a diagnostic radiology residency at University of Illinois Medical Center in Chicago (2009) and a vascular and interventional radiology fellowship at Stanford University Hospital in 2010.
In research, Dr. Shah was appointed as affiliated faculty at the Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI) and was awarded an AIMI grant to investigate machine learning in radiomics which has led to publications on machine learning in radiomics for lung cancer. He has published research on hepatocellular carcinoma, small-cell lung cancer, and embolic therapies.