How Machine Learning Can Improve Medical Outcomes
Eric Oermann, MD, Neurosurgeon at Mount Sinai Health System, New York, discusses a slightly different topic in this talk: How machine learning can improve medical outcomes. He begins by emphasizing that deep learning is automated feature engineering and that deep neural networks learn hierarchical feature representations. To apply this to the work of physicians, Dr. Oermann poses the questions: What problems do we face as physicians and how can we translate those into machine learning to create better outcomes? He then provides examples of areas where machine learning could play a role in the hospital: Assessment in the ICU, brain biopsies, faster interpretation of imaging and AI augmented radiosurgery (AIAR).
About the speaker
Eric Oermann, MD
Mount Sinai School of Medicine, New York City, USA
Instructor of Neurological Surgery in the Mount Sinai Health System and the Director of AISINAI, Mount Sinai’s artificial intelligence research group.
He studied mathematics at Georgetown University with a focus on differential geometry. Prior to attending medical school, Dr. Oermann spent six months with the President’s Council on Bioethics studying human dignity under the mentorship of physician-philosopher Edmund Pellegrino.
Dr. Oermann has won numerous awards for his scholarship including fellowships from the American Brain Tumor Association and Doris Duke Charitable Research Foundation where he was first exposed to neural networks and deep learning.
He has published over sixty manuscripts spanning basic research on machine learning, tumor genetics, and the philosophy of medicine. As a PGY-2, Dr. Oermann was selected as one of Forbes Magazine’s 30 Under 30 for his work in using machine learning to develop prognostic models for cancer patients. Dr. Oermann completed a postdoctoral fellowship at Google (Google Health / Verily Life Sciences).
He is interested in weakly supervised learning, reinforcement learning with imperfect information, and in building artificial neural networks that more accurately model biological neural networks. As a neurosurgeon, he is also interested in the application of deep learning to solve a wide range of problems in the medical sciences and improving clinical care.
Eric Karl Oermann, MD
Department of Neurosurgery
Mount Sinai Health System
1468 Madison Avenue
Annenberg Building, 8th Floor – Room 40
New York, New York 10029
Management News for Patients with Functional Disorders
Radiosurgery Registry Data Mining: Process and Results from the First 3000 Patients
Expanding Novalis Radiosurgery Applications for Functional Indications
Evaluation of New Software for OAR Contouring
Benefits of Data Enrichment Solutions for Gamma Knife Radiosurgery Planning
Effects of MRI Distortions for Target Definitions in Functional Treatments
Clinical Advantages of Elements with ExacTrac
Benefits of Implementing Automation and Outcome Measures in Modern Radiosurgery Practices
Interested in watching this clinical talk? Please click on the button “WATCH Video” to enter your contact details and to have access to two additional videos from the member-only Novalis Circle. Your contact information will only be used to contact you about Novalis Video related updates.WATCH Video
Thank you for watching! Please enter your contact details below to continue watching and to have access to two additional videos from the member-only Novalis Circle. Your contact information will only be used to contact you about Novalis Circle video related updates.Watch video No Thanks
Already Novalis Circle Member?Member Login