Geoff Tison, M.D. M.P.H. is a Cardiologist and an Associate Professor in the Division of Cardiology at the University of California, San Francisco (UCSF). Dr. Tison earned his Sc.B. in Neuroscience at Brown University and received his M.D. and M.P.H. degrees from the Johns Hopkins Schools of Medicine and Public Health. He completed internal medicine residency at the Johns Hopkins Hospital, and subsequently completed fellowships in clinical cardiology, advanced echocardiography and preventive cardiology at UCSF. He also served as the first UCSF “Digital Cardiology” fellow, where his efforts were focused on validating and improving various digital, mobile and medical-device-based technologies to achieve the greatest impact in clinical care and medical research.
Research Interests
Dr. Tison brings expertise in clinical research, advanced machine learning algorithms and digital health to bear to further his research goals in cardiovascular disease prevention. An expert in machine learning and artificial intelligence as applied to medicine, he obtained formal training in epidemiology, statistical methods, machine learning and clinical research during his tenure at the Johns Hopkins Bloomberg School of Public Health and as a National Institutes of Health T32 scholar. He has led multiple research projects in large cohorts such as the Multi-Ethnic Study of Atherosclerosis and the Women’s Health Initiative. Dr. Tison is an investigator in the UCSF Health eHeart study and leads several clinical research studies at UCSF. Dr. Tison’s current interests include applying machine learning and deep-learning techniques to large-scale electronic health data from heterogeneous sources in order to achieve the goal of personalized cardiovascular prognosis and disease prevention.
Clinical Interests
Dr. Tison is a non-invasive cardiologist with expertise in preventive cardiology and advanced echocardiography, including applications of transesophageal echocardiography in structural interventions such as transcatheter aortic valve replacement.
UC Noyce Initiative project
“Development of Novel, Multimodal, Physiologically Focused Artificial Intelligence Algorithms.” This project will design medical AI algorithms by establishing a novel, multimodal, physiologically focused deep neural networks architecture optimized for medical tasks. This will then be validated using heart failure data. This could allow for earlier and more accurate heart failure diagnosis.