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

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
Instructor
Department of Neurosurgery
Mount Sinai Health System
1468 Madison Avenue
Annenberg Building, 8th Floor – Room 40
New York, New York 10029

T: 212.241.5055
E: eric.oermann@mountsinai.org
www.mountsinai.org/neurosurgery


Related Videos

  • Management News for Patients with Functional Disorders

    Management News for Patients with Functional Disorders

    Antonio A.F. De Salles, MD, PhD
    University of California Los Angeles, USA; Hospital do Coração (HCor)
    Show video


  • Radiosurgery Registry Data Mining: Process and Results from the First 3000 Patients

    Radiosurgery Registry Data Mining: Process and Results from the First 3000 Patients

    Douglas Kondziolka, MD, MSc
    NYU Langone Medical Center, New York City, USA
    Show video


  • Expanding Novalis Radiosurgery Applications for Functional Indications

    Expanding Novalis Radiosurgery Applications for Functional Indications

    Michael Dally, MD
    ICON Cancer Centre, Epworth Hospital, Melbourne, Australia
    Show video


  • Evaluation of New Software for OAR Contouring

    Evaluation of New Software for OAR Contouring

    Olaf Wittenstein
    Universitätsklinikum Schleswig-Holstein Campus Kiel
    Show video


  • Benefits of Data Enrichment Solutions for Gamma Knife Radiosurgery Planning

    Benefits of Data Enrichment Solutions for Gamma Knife Radiosurgery Planning

    Selçuk Peker, MD
    Koc University, Istanbul, Turkey
    Show video


  • Effects of MRI Distortions for Target Definitions in Functional Treatments

    Effects of MRI Distortions for Target Definitions in Functional Treatments

    Thierry Gevaert, PhD
    Universitair Ziekenhuis Brussel
    Show video


  • Early Validation of ExacTrac Dynamic: New Features and Accuracy of the System

    Early Validation of ExacTrac Dynamic: New Features and Accuracy of the System

    Fatma Rahma, MSc
    Rigshospitalet, Denmark
    Show video


  • Frameless Radiosurgery Clinical Validation for Functional Radiosurgery Patients

    Frameless Radiosurgery Clinical Validation for Functional Radiosurgery Patients

    Aidnag Diaz, MD
    Rush University Medical Center, Chicago, USA
    Show video


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

Keep Watching

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.

  • Please read the following notice
    I have read the privacy policy. I agree that my information and data will be collected and stored electronically in order to answer my enquiry. Please note: You can revoke your consent at any time with effect for the future by emailing legal@brainlab.com.

Watch video No Thanks

Already Novalis Circle Member?

Member Login

Register now

Thank you for your interest in our Novalis Circle videos. Full-access to the media library is currently available for Novalis Radiosurgery users only. If you are a customer, please register or login to continue.

Register Now Login