Radiology Reimagined: Advancing Clinical Practice Through AI Innovation (2025)

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Availability

Registration ends on October 17, 2025

Expires on Jan 16, 2026

Hospital de la Santa Creu i Sant Pau
Carrer de Sant Quintí, 89, Horta-Guinardó, 08025 Barcelona, Spain

Oct 16 - Oct 17, 2025 CET

Cost

Basic: $475.00

Standard: $425.00

Full Access: $400.00

Member-in-training: $250.00

Non-Member: $650.00

Credit Offered

No Credit Offered

To register for this course, please click Create Account or Login if you already have an account with RSNA.

This two-day RSNA Spotlight course brings together global leaders in radiology and artificial intelligence to explore the real-world application of AI tools across diverse practice settings. Through didactic education, case-based learning, and discussions, attendees will gain insight into the lifecycle of AI evaluation and deployment, and the role of generative AI. Participants will leave equipped to lead evidence-based AI implementation and advocate for innovation that enhances both clinical outcomes and professional practice.

Course Language
English

Course Dates & Location
Thursday and Friday, October 16-17, 2025

9:00am-5:00pm Local Time

Hospital of Sant Pau, Barcelona, Spain

Hotel Information
Reserve your hotel room at the Hotel Catalonia Atenas to receive a 10% discount on your stay!
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Cost
Basic Member: $475.00
Standard Member: $425.00
Full Access Member: $400.00
Member-in-training: $250.00
Non-Member: $650.00

Nina Kottler, MD, MS, FSIIM

Dr. Kottler has been a practicing radiologist specializing in emergency imaging for over 20 years. Combining her clinical experience with a graduate degree in applied mathematics, she has been using technological innovation to drive value in radiology. As the first radiologist to join Radiology Partners, Dr. Kottler has held multiple leadership positions within her practice and is currently the associate Chief Medical Officer for Clinical AI. Dr. Kottler is also an associate fellow at the Stanford AIMI Center and serves on committees for RSNA, ACR, RADeqal, and SIIM. Dr. Kottler is passionate about promoting diversity and creating a culture of belonging. As an industry expert, Dr. Kottler consults for companies in aerospace, materials science, and healthcare and is a frequent international lecturer discussing imaging AI.

Charles E. Kahn, MD, MS

Dr. Charles Kahn is professor and vice chair of radiology at the University of Pennsylvania. His research interests are in artificial intelligence (including deep learning, natural language processing, knowledge representation, and probabilistic reasoning), health services research, and information standards. He has authored more than 160 peer-reviewed journal articles and given more than 200 invited lectures. He currently serves as editor of Radiology: Artificial Intelligence.

Amine M. Korchi, MD

Dr. Amine Korchi is a Swiss board-certified radiologist and neuroradiologist based in Geneva, seamlessly integrating clinical practice with leadership roles in the radiology AI industry and HealthTech ventures. He is deeply committed to advancing radiology through the integration of artificial intelligence. Dr. Korchi completed his radiology training at Geneva University Hospitals and pursued specialized studies in medical technology management at EPFL. He has served as a principal investigator and researcher at the University of Montreal and brings additional experience in strategy consulting and venture capital. His contributions to HealthTech innovation and radiology AI have been recognized by Sifted, a Financial Times-backed media outlet, naming him among the top 25 global HealthTech experts. Additionally, The Imaging Wire recognized him as one of the top 10 radiology AI Key Opinion Leaders.

Radiology Reimagined: Advancing Clinical Practice Through AI Innovation
October 16-17, 2025
Thursday, October 16, 2025
8:00 am - 9:00 am	Registration
9:00 am - 9:30 am	Welcome Messages and Introduction to the Course
9:30 am - 10:30 am	Basic Concepts AI
10:30 am - 11:00 am	Interactive Session
11:00 am - 11:15 am	Break
11:15 am - 1:00 pm	Pre-Deployment AI
1:00 pm - 2:00 pm	Lunch
2:30 pm - 4:30 pm	Implementing AI
4:30-5:00pm	Q&A
5:00pm	Day 1 Closing Remarks
5:00 - 6:00pm	Networking Reception
Friday, October 17, 2025
9:00 am - 9:15 am	Review of Day 1, Overview of Day 2
9:15 am - 10:45 am	Post Deployment AI
10:45 am - 11:15am	Networking Break
11:15am - 12:45 pm	AI Topics of Interest Block 1
1 pm - 2 pm	Lunch
2 pm - 3:15pm	AI Topics of Interest Block 2
3 pm - 3:30 pm	Faculty Panel Discussion
3:30-4:30 pm	Put It Into Practice - Evaluation of AI Accuracy Exercise
4:30pm	Closing Remarks

By the end of this course, participants will be able to:

Understand the foundational concepts of AI in medical imaging, including distinctions between traditional algorithms and foundation models, the evolving role of radiologists, and how AI supports the shift from population health to precision medicine.
Gain practical tools to facilitate clinical implementation, covering AI use case identification, model selection frameworks, key performance metrics, ethical considerations, data orchestration, and best practices for integrating AI into radiology workflows.
Explore strategies for ongoing impact, including continuous monitoring of AI models, addressing automation bias, demonstrating value through outcomes, navigating ROI and payment models, and preparing the next generation of radiologists.

Name changes are not allowed; new registration and payment are required.
Registration is non-transferable.
All registration cancellations must be made in writing to customerservice@rsna.org by September 19, 2025 to receive a refund.
Registration cancellations received by September 19, 2025 will result in a refund of paid fees, minus a $50 administrative fee.
Refund requests received after September 19, 2025 will not be accepted or refunded.
No refunds will be issued for registrations purchased and not used.

Questions? Please contact customerservice@rsna.org.

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.