The purpose of this session is to explain what are machine and deep learning, their role in radiology research and practice, and how to prepare for them. This session will emphasize how radiologists can approach machine learning and deep learning, separating hype from reality. It will demonstrate how these self-improving algorithms will help radiologists, and the challenges of producing valid and useful computer models. Finally, it will present a roadmap for machine and deep learning in radiology, and ACR's roles as this technology evolves.
Learning Objectives Upon completion of this session, participants will be able to:
Describe the basics of machine learning and deep learning.
Discuss the roles these software tools will play in radiology, and how they can help radiologists.
Identify what is needed to bring valid and useful machine and deep learning algorithms into widespread clinical practice, and ACR's roles as this technology evolves.
Presentations What Should Radiologists Think About Machines That Think 10:00 AM - 10:25 AM Duration: 25 minutes Speaker Raym Geis, MD, FACR
A Deep Dive into Deep Learning 10:25 AM - 10:50 AM Duration: 25 minutes Speaker Garry Choy, MD
Radiology's Clinical Data Science Road Map 10:50 AM - 11:35 AM Duration: 45 minutes Speaker Keith Dreyer, DO, PhD, FACR
Questions and Answers 11:35 AM - 12:00 PM Duration: 25 minutes Speaker(s) Raym Geis, MD, FACR Garry Choy, MD Keith Dreyer, DO, PhD, FACR