Dr. Rima Arnaout is Assistant Professor of Medicine, member of the Bakar Computational Health Sciences Institute, and faculty in the graduate program in Biological and Medical Informatics at the University of California, San Francisco. She is a physician-scientist with a strong background in genetics, clinical research and programming, and a practicing cardiologist board-certified in multi-modality cardiovascular imaging. Dr. Arnaout is currently developing computational methods to bring precision phenotyping to cardiovascular imaging for both research and clinical use. Dr. Arnaout is the recipient of the Sarnoff Cardiovascular Research Foundation Fellow and Scholar awards. She completed her undergraduate degree at the Massachusetts Institute of Technology, her MD at Harvard Medical School, her internal medicine residency at Massachusetts General Hospital, and her cardiology fellowship and imaging training at the University of California, San Francisco.
Machine learning is revolutionizing the potential of imaging for precision medicine. To realize that potential, we need not only expand rigorous ML research, but also solve several big-picture challenges in this emerging field. This panel will feature experts from research and industry to briefly highlight several use cases and then discuss bigger challenges such as data security and sharing, models for federated development of machine learning solutions, and the need for shared platforms and tools.