Geoff Tison, MD, MPH is a Cardiologist and Assistant Professor at the University of California, San Francisco (UCSF). His research focuses on cardiovascular prevention, using statistical and machine learning methods to analyze large-scale health data for disease prevention and phenotyping. He obtained formal training in machine learning, statistics, epidemiology and clinical research during his tenure at Johns Hopkins and as a National Institutes of Health T32 scholar. Dr. Tison received MD and MPH degrees from the Johns Hopkins Schools of Medicine and Public Health, completed internal medicine training at the Johns Hopkins Hospital, and fellowships in cardiology, advanced echocardiography and preventive cardiology at UCSF.
Remote sensors that capture health-related data, from CardioMEMs to the Apple Watch, are increasingly common in the modern healthcare landscape. This session will examine both existing applications of remote sensors that derive actionable clinical insights and the future potential for these tools for precision medicine.