RSNA marked a turning point for radiology AI.

For years, the exhibit hall was filled with impressive algorithms and flashy demos. This year, the focus shifted to something far more important: workflows, interoperability, and follow-up.

Radiology leaders are no longer asking, “Is this cool?” They are asking:
Can it integrate?
Can we measure outcomes?
What happens after a finding is detected?

As shared on After the Chaos, one of the biggest risks in today’s AI adoption is turning on detection without a plan for care. A worklist is not a follow-up. Without orchestration, AI can actually increase liability instead of improving outcomes.

Another major shift is the demand for proof over hype. Leaders want real clients, real workflows, and real results—not marketing promises.

The future of radiology AI isn’t about one-off algorithms or closed platforms. It’s about interoperable, end-to-end workflows that ensure patients actually receive the care their images identify.

At RSNA, the message was loud and clear: Interoperability is now the competitive advantage.

Episode chapter guide:

01:50 Insights from RSNA: Technology and Quality Improvement
04:41 Navigating Technology Selection in Radiology
08:39 The Evolution of AI in Radiology
12:23 Interoperability and Workflow Integration
17:59 Demand for Proof: Moving Beyond Hype
20:33 Care Orchestration and Follow-Up Challenges