Machine Learning (ML) and Artificial Intelligence (AI) in Radiology
Join us online February 5, 11:00 am–4:00 pm, Eastern Time, for our Machine Learning and Artificial Intelligence in Radiology Virtual Symposium, which will include both the fundamental aspects of this topic as well as its current and future clinical applications in radiology. Participants will gain a broad understanding of this topic and will be better able to evaluate and use AI applications in their own practices.
- Earn up to 4.25 CME and 4.25 live Self-Assessment (SA-CME) credits during and after the presentation through April 2
- Learn from accomplished faculty
- Gain insights into machine learning and artificial intelligence in radiology
- And more!
Michael A. Bruno, MD, MS
Professor and Vice Chair for Quality & Safety, Penn State Hershey
|John Banja, PhD
||Steven Li, MA
||Anthony Reeves, PhD
View Full Schedule and Faculty
|David Chartash, PhD
||Charles Kahn, MD
ARRS is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education activities for physicians.
ARRS designates this live activity for a maximum of 4.25 AMA PRA Category 1 Credit(s)™ and 4.25 American Board of Radiology©, MOC Part II, live Self-Assessment CME (SA-CME) credits. Physicians should claim only the credit commensurate with the extent of their participation in an activity.
Credit On-Demand Sessions: ARRS designates this enduring material for a maximum of 4.25 AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
Register today to attend the Machine Learning (ML) and Artificial Intelligence (AI) in Radiology Virtual Symposium! See table below for rates.
Your membership must be current at the time of registration as well as for the duration of the Symposium in order to receive the discounted member rate. Nonmembers who wish to apply for ARRS membership and pay the member registration fees must do so no later than January 29, 2021.
See below for more information on becoming a member.
The Virtual Symposium provides live broadcasting of symposium sessions online. All sessions will broadcast in Eastern Time Zone. Sessions and credit on demand will also be available to virtual registrants February 5–April 2.
Through February 5
|ARRS In-Training Member
Virtual Registration Policies
Join to Receive the Member Rate: To join and receive the member rate, submit a membership application online and fax your registration form to 703-729-4839 and indicate that you submitted an online membership application. You may also fax a completed membership form with your registration form. In order to be eligible to receive the discounted member registration fees, the membership application must be received no later than January 29, 2021.
Register: Register online now or by completing the registration form and fax it to 703-729-4839 or mail it to ARRS, Meeting Registration, 44211 Slatestone Court, Leesburg, VA 20176-5109. Please contact ARRS if you have any questions at 866-940-2777 or 703-729-3353 or firstname.lastname@example.org. Registrations by phone will not be accepted.
Audio/Video Recording Policy: Audio/Video recording including screen capturing or photography of any ARRS educational offering of any type is not allowed. Violations of this policy will be subject to access termination.
Virtual Cancellation Policy: Written cancellation requests received by January 29, 2021, will be refunded by check after the meeting, minus a 25% cancellation fee. The 25% cancellation fee is nonrefundable. After January 29, 2021, absolutely no refunds will be issued.
Faculty & Schedule
Machine Learning (ML) and Artificial Intelligence (AI) in Radiology Virtual Symposium
Course Director: Michael A. Bruno, MD, MS
All times reflect Eastern Time Zone
This schedule is subject to change.
After attending this symposium, the learner will be able to:
- Discuss the basics of Machine Learning and Artificial Intelligence
- Explain how AI algorithms can be affected by bias
- Recognize the impact of AI systems on clinical reasoning
- Evaluate new AI claims and offerings and separate the hype from reality in Radiology AI
- Discuss how Radiology AI is being used currently in cardiothoracic imaging
- Reference future developments in AI in Radiology
- Recognize some ethical considerations in Radiology AI
The target audience for this activity is radiologists, radiologists in-training, and other health professionals with an interest in machine learning and artificial intelligence in radiology.
On-Demand Sessions with Credit
Can’t watch a session live? Don’t worry.
All sessions will be available on demand with credit for all registrants. Simply view the recordings and complete the evaluation to earn your credit. In addition, you can earn Self-Assessment credits by completing the CME questions for each session.
When can you access On-Demand Sessions?
On-demand sessions will be available through April 2. The complete recording of the Virtual Symposium will be available within 24 hours of the live session’s completion. Once registrants have received the email that the recorded sessions are available, registrants can begin to view on-demand sessions. Session recordings and credit on demand will be available until Friday, April 2, 2021.
Prior to the symposium, all registrants will receive information by email on how to access and view on-demand sessions and claim credit.
Disclosure of Commercial Interest
The following faculty members and planners have indicated that they do not have a financial relationship with a commercial organization that may have a direct or indirect interest in the subject matter being presented, or with any commercial organization that has provided funds for the educational activity:
- John Banja has nothing to disclose.
- Michael Bruno has nothing to disclose.
- Charles Kahn has nothing to disclose.
- David Chartash has nothing to disclose
- Kerry Davis has nothing to disclose.
- Steven Li has nothing to disclose
- John Leyendecker has nothing to disclose.
- Jeremiah Long has nothing to disclose.
- Courtney Moreno has nothing to disclose.
- Christopher Neumann has nothing to disclose.
- Anthony Reeves has nothing to disclose.