The Implementation Gap: What Actually Works When Hospitals Deploy AI
NeuroEdge Nexus — Season 1, Week 4 (October 2025) PART 2
The greatest barrier is not technical failure, but the failure to translate potential into practice
PART 2
Last article, we examined why regulatory frameworks block even validated AI from reaching patients. But regulation is only one barrier. Even when AI tools receive FDA approval, most never reach clinical practice. This week: what a Dutch academic medical center built over three years to solve infrastructure, workflow, and cultural challenges—and what it means for neurology.
The Problem Beyond Regulation
Last week, we discussed why regulatory compliance consumes 6-12 months per AI application A. Kim et al., 2024) , why neural data sovereignty remains unresolved, and why FDA approval pathways aren’t designed for continuously learning algorithms.
But here’s the uncomfortable truth: Even when AI tools have regulatory approval, they still don’t deploy.
Multiple FDA-cleared AI algorithms exist for stroke detection, yet adoption rates remain below 30% in eligible hospitals. The barrier isn’t regulation—it’s infrastructure, workflow disruption, and organizational resistance.
Between 2019 and 2022, a large academic medical center in the Netherlands spent three years systematically addressing these barriers. Their experience—documented through 43 days of observations, 30 meetings, 18 interviews, and 41 analyzed documents—reveals what actually works when hospitals attempt to deploy AI at scale. A (Kim et al., 2024).
The lessons apply directly to neurology, where promising AI tools for MS monitoring, seizure detection, and Parkinsonian disorder classification face identical implementation challenges.
