How AI Can Serve as a Research Support Companion in Radiology

Radiologists don’t always stay within one specialty. In many environments, especially across academic and community settings, they’re reading a wide range of cases.

In this webinar clip, Adam E. Flanders, MD, RSNA Board Member and Professor of Radiology at Thomas Jefferson University, and Melissa A. Davis, MD, MBA, Vice Chair of Medical Informatics at Yale University, discuss how that reality shapes the need for support within the workflow.

As Dr. Flanders explains, even experienced radiologists encounter cases outside their core area. In those situations, the typical approach is to consult a colleague or look up additional information.

The concept discussed here is using AI as a “companion” in those moments. Dr. Flanders describes it as having someone alongside you who can help walk through the thought process, particularly when interpreting something less familiar.

This need isn’t limited to any one group. While academic radiologists may focus on subspecialties, they still reference information regularly. At the same time, community radiologists and emergency radiologists often read across a broader range of cases, where access to quick, relevant context can be especially useful.

As Dr. Davis notes, these tools can support both groups. Her team has seen this dynamic in practice. Tools initially requested by community radiologists have also been adopted by academic teams, highlighting how needs can overlap across different environments.

The goal is to reduce the time spent searching for information and focus more on patient care.  

This clip is part of a broader discussion on how radiology workflows are evolving. To hear more, you can access the full on-demand webinar.