Value is in the A-Eye of the Beholder: Defining and Measuring Value of Imaging Artificial Intelligence

Artificial intelligence in imaging has moved beyond early pilots, but many organizations still struggle to adopt AI at scale due to uncertainty around value, cost-effectiveness, and return on investment. These concepts are often borrowed from management science and applied inconsistently in healthcare settings, leading to confusion and stalled decision-making. This session addresses that gap by helping attendees understand why and how AI value should be evaluated differently across clinical, operational, and patient-centered contexts, and why meaningful value may extend beyond purely financial metrics. 

Participants will gain clear, practical frameworks to define and compare “value,” “ROI,” and “cost-effectiveness” as they apply to imaging AI. Through real-world clinical and operational examples, attendees will learn how to assess diagnostic performance, workflow impact, user experience, and downstream effects alongside integration, maintenance, and sustainability costs. The session contrasts traditional cost-effectiveness approaches with pragmatic evaluation methods that better reflect day-to-day imaging operations, equipping participants with a rubric to critically interpret AI performance and vendor claims. 

Attendees will also explore how value differs across clinical interpretation tools, operational workflow AI, and opportunistic screening applications, including impacts on quality, efficiency, satisfaction, population health, and institutional performance. Interactive elements encourage participants to test their assumptions, compare perspectives, and actively contribute to discussion. By the end of the session, attendees will leave with actionable strategies to evaluate, justify, and design AI adoption plans grounded in measurable, real-world impact rather than abstract promises.  

Objectives

• Define key concepts of value, return on investment (ROI), and cost-effectiveness in the context of imaging AI. 
• Differentiate between approaches to measure value for clinical versus operational AI solutions in imaging. 
• Describe real-world strategies for evaluating the value of AI solutions in imaging workflows. 
• Recognize how non-financial outcomes, such as patient outcomes and provider workflow improvements, contribute to AI value assessment.