Description

Machine learning, deep learning and big data affect many areas, from self driving cars to face recognition to winning at Jeopardy. These advanced data science tools will soon come to radiology. This course will introduce radiologists to machine and deep learning: how they are developed; information they may provide; and the roles radiologists, imaging informaticists and the ACR should play to develop and use computers to help interpret medical images.

Learning Objectives:

1. Discuss the basics of machine learning (ML) and deep learning (DL).
2. Identify issues surrounding ML and DL use for radiology, and how the technology may impact radiology.
3. Recognize the role of radiologists and the ACR in the development and use of ML and DL.
4. Define how a data-enabled radiology report will enable radiologists to supervise the flow of many types of imaging data into the clinical environment.

Subspecialties

• General Radiology

Speaker(s):

• Tarik Alkasab, MD, Ph.D
• Keith Dreyer, DO, PhD, FACR
• Ross Filice, MD

Moderator(s):

• J. Raymond Geis, MD, FACR

Disclosures

• J. Raymond Geis, MD, FACR : <p>J. Raymond Geis: Stock Options-Montage</p>
• Tarik Alkasab, MD, Ph.D : <p>Tarik Alkasab: Consultant-Radiology Consulting Group</p>
• Keith Dreyer, DO, PhD, FACR : <p>Keith Dreyer: Consultant-Merge Healthcare</p>
• Ross Filice, MD : <p>Ross Filice: None</p>