Delivering Trusted Clinical Insights at the Point of Interpretation
Radiologists today face increasing pressure — from rising imaging volumes to more complex cases and expanding clinical knowledge.
In this clip, Demetri Giannikopoulos, Chief Innovation Officer at Rad AI, demonstrates how RSNA Ventures and Rad AI are working together to address these challenges by bringing trusted clinical insights directly into the radiologist’s workflow.
The approach combines AI-powered reporting with in-workflow diagnostic support.
As shown in the demo, radiologists can interact with information directly within the reporting interface — accessing relevant differentials, classification systems and supporting clinical context without leaving their workflow.
Rather than searching for information across multiple systems, the goal is to surface trusted, peer-reviewed RSNA content at the point of interpretation.
This allows radiologists to engage with relevant insights in real time, aligned to the case they are working on.
The integration is designed to support several key areas:
- Reducing cognitive load during interpretation
- Improving consistency and reporting quality
- Providing access to trusted clinical knowledge without adding workflow complexity
Importantly, the experience is embedded within the reporting process itself.
This clip is part of a broader discussion on the RSNA Ventures and Rad AI collaboration, including its strategic vision and potential clinical and operational impact. To hear more, you can access the full on-demand webinar.
