Geoff Tison, MD, MPH

HS Assist. Clinical Professor

Cardiology UCSF Health

Geoff Tison, M.D. M.P.H. is a Cardiologist and an Assistant 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.

  1. Assessment of Accelerometer-Based Physical Activity During the 2017-2018 California Wildfire Seasons.
  2. Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): A Checklist: Reviewed by the American College of Cardiology Healthcare Innovation Council.
  3. A digital biomarker of diabetes from smartphone-based vascular signals.
  4. Worldwide Effect of COVID-19 on Physical Activity: A Descriptive Study.
  5. Physical activity and atrial fibrillation: Data from wearable fitness trackers.
  6. Artificial Intelligence in Cardiovascular Imaging.
  7. The Rise of Open-Sourced Machine Learning in Small and Imbalanced Datasets: Predicting In-Stent Restenosis.
  8. Pulmonary arterial capacitance predicts outcomes in patients with pulmonary hypertension independent of race/ethnicity, sex, and etiology.
  9. Association of Machine Learning-Derived Phenogroupings of Echocardiographic Variables with Heart Failure in Stable Coronary Artery Disease: The Heart and Soul Study.
  10. Will the smartphone become a useful tool to promote physical activity?
  11. Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery.
  12. Real-world heart rate norms in the Health eHeart study.
  13. Echocardiographic determination of pulmonary arterial capacitance.
  14. Publisher Correction: Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network.
  15. Temporal patterns of self-weighing behavior and weight changes assessed by consumer purchased scales in the Health eHeart Study.
  16. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network.
  17. Fully Automated Echocardiogram Interpretation in Clinical Practice.
  18. Identifying heart failure using EMR-based algorithms.
  19. Passive Detection of Atrial Fibrillation Using a Commercially Available Smartwatch.
  20. Thoracic extra-coronary calcification for the prediction of stroke: The Multi-Ethnic Study of Atherosclerosis.
  21. Measurement of brachial artery endothelial function using a standard blood pressure cuff.
  22. Perceptions, Information Sources, and Behavior Regarding Alcohol and Heart Health.
  23. Relation of Anthropometric Obesity and Computed Tomography Measured Nonalcoholic Fatty Liver Disease (from the Multiethnic Study of Atherosclerosis).
  24. Multisite extracoronary calcification indicates increased risk of coronary heart disease and all-cause mortality: The Multi-Ethnic Study of Atherosclerosis.
  25. The relationship of insulin resistance and extracoronary calcification in the multi-ethnic study of atherosclerosis.
  26. Usefulness of baseline obesity to predict development of a high ankle brachial index (from the Multi-Ethnic Study of Atherosclerosis).
  27. Atherosclerosis imaging in multiple vascular beds--enough heterogeneity to improve risk prediction?
  28. Influences of general and traditional Chinese beliefs on the decision to donate blood among employer-organized and volunteer donors in Beijing, China.
  29. Ethanol impairs insulin-stimulated neuronal survival in the developing brain: role of PTEN phosphatase.