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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.

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Barnett M et al. A real-world clinical validation for AI-based MRI monitoring in multiple sclerosis. NPJ Digit Med. 2023;6:196.

PART 1

Between 2019 and 2022, a large academic medical center in the Netherlands spent three years attempting to implement artificial intelligence across its radiology department. What they discovered reveals why even validated algorithms struggle to reach clinical practice—and why regulatory frameworks, not technical performance, determine whether AI helps patients.

Last week, we discussed AI fundamentals and infrastructure challenges. This week, we examine the regulatory and organizational frameworks that determine whether AI reaches clinical practice—and what institutions learned by navigating these barriers for three years.

The Reality Check

A comprehensive longitudinal study published in Insights into Imaging (2024) documented the real-world challenges of implementing AI in clinical radiology at a major European academic medical center. Over three years, researchers conducted 43 days of work observations, 30 meeting observations, 18 interviews, and analyzed 41 documents to understand what actually happens when hospitals try to deploy AI tools. A holistic approach to implementing artificial intelligence in radiology | Insights into Imaging | Full Text The findings were instructive: before establishing proper infrastructure, implementing a single AI application took 18-24 months, with the majority of that time consumed by legal documentation, regulatory compliance, contractual negotiations, and data governance frameworks—not algorithm validation or clinical testing. A holistic approach to implementing artificial intelligence in radiology | Insights into Imaging | Full Text This was not a failure of artificial intelligence. This was a failure of regulatory and organizational systems to keep pace with technological capability.

The institution eventually succeeded by building what they called a “holistic approach”—addressing regulatory compliance, data sovereignty, technology infrastructure, workflow integration, and organizational culture simultaneously rather than treating AI deployment as a purely technical problem. A holistic approach to implementing artificial intelligence in radiology | Insights into Imaging | Full Text Their experience offers critical lessons for neurology, where similar AI tools promise significant clinical value yet face identical regulatory and governance barriers.

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If the algorithms are so sophisticated, why do they often fail to reach patients?

Artificial intelligence (AI) in neuroscience is often portrayed as a revolution, but the reality is more tempered. Many of you have followed this journey from LinkedIn, drawn by reflections on AI, neuroscience, and ethics. As a NeuroEdge Nexus LINK, we encourages deeper exploration of the emerging interface of AI and neurology, clinical neurophysiology and neuroscience focusing on analysis, interpretation, and critical thinking rather than prescriptive instruction.

The imperative is clear: AI in neuroscience is not merely academic. Its promise can only be realized when supported by robust computational infrastructure, interoperable platforms, regulatory frameworks, and ethically grounded oversight. Without these scaffolds, even the most sophisticated algorithms remain theoretical — insightful yet unable to impact patient outcomes.

This season, “Foundations & Neural Infrastructure "LINK, will explore how computational, regulatory, and ethical frameworks are indispensable for translating AI into meaningful clinical outcomes. Our year-long journey alternates between foundational essays (data, governance, infrastructure, ethics) and practical applications in neurology, clinical neurophysiology, and neuromodulation. Only by understanding both sides of these layers can one responsibly deploy AI in practice.

Healthcare professionals face a challenge: much of neuroscience research remains locked in dense academic literature, while popularized content oversimplifies mechanisms, offering little actionable insight. At the same time, AI tools and neuromodulation technologies are evolving rapidly, outpacing conventional communication channels.

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