To help you keep up with state-of-the-art technology, RSNA presents AI Webinars. This series of four, 60-minute sessions focuses on artificial intelligence and machine learning.

Designed for radiologists from all practice settings in mind, the series begins with a broad introduction to artificial intelligence and machine learning. Later courses will discuss the current state and future perspectives of the technology for radiology professionals.

Costs: Member: $35; Non-member: $50

Upcoming sessions

View the live, online event. This format offers a question-and-answer session with the moderator and faculty members. CME credit is available.

AI: an Ally or an Enemy? A Roundtable Discussion

Date: Feb. 21, 2019, 11 a.m. to 12 p.m. CT
Moderator: Dr. Luciano Prevedello
Additional Speakers: Dr. Curt Langlotz, Dr. Paul Chang and Dr. Adam Flanders

At the conclusion of the course learners will be able to:

  • Describe the strengths and limitations of deep learning for detection and diagnosis in radiology.
  • Explore how the implementation of AI in radiology over the next decade will significantly improve the quality, value, and depth of radiology's contribution to patient care and population health. 


On demand

View the webinars on demand starting one month after the live event. SA-CME credit is available.

Intro to AI and Machine Learning: Why All the Buzz?

Moderator and speaker: Dr. Curt Langlotz
Additional speaker: Dr. Matthew Lungren

At the conclusion of the course learners will be able to:

  • Understand the origins of artificial intelligence and machine learning and their application to medical imaging
  • Predict how artificial intelligence will change the practice of radiology using current examples
  • Assess the shortcomings of artificial intelligence that may limit its applicability


Current State and Future Perspectives of AI

Moderator: Dr. Paul Chang
Additional Speaker: Dr. Luciano Prevedello and Mr. Abdul Halabi

At the conclusion of the course learners will be able to:

  • Describe how a realistic deep learning and artificial intelligence perspective can add value to radiology.
  • Identify the significant challenges with respect to practical implementation of deep learning/artificial intelligence offerings by existing radiology workflow and existing information technology infrastructure.
  • List strategies for preparing the radiology department and IT for deep learning/artificial intelligence.
  • Analyze specific examples of AI that have been implemented into clinical practice and training.
  • Describe the role and responsibility of various AI technology vendors that are working with radiologists on implementation.


Future Applications of AI

Moderator: Dr. Adam Flanders
Additional speakers:
Dr. Charles E. Kahn, Dr. Marc Kohli and Dr. J.R. Geis

At the conclusion of the course learners will be able to:

  • Outline resources that are available for ongoing learning as it relates to the field of AI research and the practical application of AI.
  • Describe future applications of AI and how radiologists can play a critical role in implementing AI into daily practice.
  • Describe the ways other than "computer vision" where AI can be used to improve radiology practice.
  • Outline tactics on how to prepare future radiologists of this change in practice.