This session explores the use of agentic artificial intelligence (AI) in radiology, focusing on interactive, autonomous AI agents that go beyond traditional task-based algorithms. Through foundational overviews and real-world case studies, attendees will understand how agentic AI is reshaping radiology workflows, improving clinical decision-making, and enabling patient-centric innovations. The session will feature expert-led lectures followed by a panel discussion, offering practical guidance for safely integrating these systems into clinical practice.
Content Areas (Codes):
The following Content Areas will be printed on the certificate for this course:
- Artificial Intelligence
- Informatics
Learning Objectives:
- Define the core principles and types of agentic AI and distinguish them from traditional AI systems.
- Describe practical applications of agentic AI in current radiology workflows.
- Identify challenges and future directions for adopting agentic AI in multidisciplinary clinical settings.
This non-CME activity is estimated to take 45 minutes to complete.
Start Date: 3/18/2026
Online Expiration Date: 3/17/2029
This educational activity was originally presented at the RSNA 2025 Annual Meeting and Scientific Assembly.
Faculty:
- Imon Banerjee, PhD
- Paulo Kuriki, MD
- George Shih, MD, MS
Planners:
- Jenny T. Bencardino, MD
- Guillermo Elizondo-Riojas, MD, PhD
- Marta E. Heilbrun, MD
- Ayden Jacob, MD, MSc
- Bertram J. Stemmler, MD
- Meike W. Vernooij, MD
- Grace G. Zhu, MD
Price:
Non-Member/Basic Member Rate: $55.00
Standard Member/Full Access Member Rate: $0.00
Refund / Exchange Policy:
RSNA will not issue any refunds or exchanges for online only versions of educational products or activities purchased online. Please review the entire product or activity description prior to purchase.
RSNA Disclaimer:
The opinions or views expressed in this activity are those of the presenters and do not necessarily reflect the opinions, recommendations or endorsement of the RSNA. Participants should critically appraise the information presented and are encouraged to consult appropriate resources for information surrounding any product or device mentioned. Information presented, as well as publications, technologies, products and/or services discussed, are intended to inform the learner about the knowledge, techniques, and experiences of RSNA faculty who are willing to share such information with colleagues. The RSNA disclaims any and all liability for damages to any individual user for all claims which may result from the use of said information, publications, technologies, products and/or services, and events.