About the Session
AI is quickly becoming part of everyday imaging workflows—but choosing how to adopt it is just as important as choosing what to adopt. This session helps attendees make sense of the build, buy, and partner options so AI decisions can be approached with clarity and confidence.
Attendees will explore how each strategy plays out in real clinical and operational settings, including radiology reporting automation, workflow optimization, and automated quantitation tools such as AIPL and 3D lab applications. Through practical examples, participants will see how factors such as cost, integration effort, regulatory considerations, technical complexity, and organizational readiness influence successful (and unsuccessful) AI implementations.
Participants will also gain insight into how AI strategy choices differ between academic and private practice environments, and why the same solution may not work equally well across institutions. By the end of the session, attendees will leave with a clear framework for evaluating AI options and selecting an approach that aligns with institutional goals, available resources, and long-term vision—whether just getting started or refining an existing AI strategy.
Objectives
- Compare the benefits and trade-offs of building, buying, or partnering for AI solutions.
- Identify key factors—including technical feasibility, cost, integration, and compliance—that influence AI strategy decisions.
- Differentiate how academic versus private practice contexts shape AI adoption and deployment.
Presented By
Bernardo Bizzo, MD
