Insights

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.

When evaluating AI partners, it's critical to look past the marketing and scrutinize the architecture.

The primary challenge is not the technology, but the complex processes of vendor onboarding, legal review, security assessments, and new procurement cycles.

Radiology has always demanded reliability: PACS. RIS. Dose monitoring. Quality metrics. Structured reports. AI is no different.