AI is evolving rapidly—from task-specific tools to intelligent systems embedded across radiology workflows. But as capabilities expand, an essential question remains: where should the line be drawn between assistance and automation? In this conversation, we explore how AI is evolving from a supportive tool into a true partner for clinicians, helping interpret data, streamline workflows, and unlock new possibilities in patient care without removing human oversight, responsibility, or clinical judgment.
Across perspectives, one theme is consistent: AI works best as a co-pilot. It can assist with detection, prioritization, and triage - but final interpretation and accountability must remain human.
The role of AI changes depending on setting. In high-volume environments, automation supports speed and consistency. In academic or teaching hospitals, AI augments learning and decision-making rather than replacing human input. Context, not capability, determines how AI should be applied.
The most effective AI systems don’t announce themselves. They integrate seamlessly into existing workflows, supporting radiologists without adding complexity or distraction.
As radiology transforms, quality has to remain at the center of everything we do.
As a reporting radiologist, you’re responsible for the final interpretation. AI can support the process, but the radiologist must remain in control and authorize the results.