Insights

This time, the message is clear: agentic AI brings new risks that traditional security controls do not address. Every recommendation in the document starts from a cautious approach. The opening line sounds more like a warning than a set of instructions.

Radiologists who handle large volumes of breast screenings each day can’t deal with extra complications. If a facility uses DeepHealth or Screenpoint, they shouldn’t have to start a separate project for each vendor. They shouldn’t need special formats or manual fixes.

Right now, too many healthcare organizations are paying a heavy "Fragmentation Tax"—rebuilding integration logic for every new model, stalling their clinical fleets, and inflating their Total Cost of Ownership.

Some identify findings clinicians may overlook, while others incorrectly flag normal cases. Over time, some models degrade as patient populations change or vendors update software.

The vendor’s dashboard was clear when one person managed it, but now no one is sure who should monitor it.

Health systems are now being asked to innovate while simultaneously managing risk, accountability, and patient safety, often without a single, unified playbook.

Most health systems lack the technical infrastructure to generate continuous evidence of AI performance. Instead, they rely on episodic, manual validation that fails to capture real-world shifts in data.

Trust does not come from validation studies, but from continuous monitoring ensuring the model performs as expected in clinical reality.