The Regulatory Trilemma: Navigating MDR, AI Act, and GDPR for Medical AI in the EU

The Regulatory Trilemma: Navigating MDR, AI Act, and GDPR for Medical AI in the EU

The commercialization of medical Artificial Intelligence (AI) in the European Union is simultaneously governed by three major regulatory pillars: the Medical Device Regulation (MDR), the proposed AI Act, and the General Data Protection Regulation (GDPR). While all three are designed to ensure safety, trust, and privacy, their operational requirements often clash, creating a significant burden on continuous innovation.

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The Static vs. Dynamic Conflict

The core conflict lies in the fundamental nature of the MDR and the AI Act:

• MDR's Static Framework: The MDR is a "static" framework, designed primarily to guarantee the safety and performance of a device at the point of release. It is ill-suited to assess continuously learning AI models, forcing the review process into a fixed, single-instance evaluation.
• AI Act's Dynamic Model: The AI Act envisions a "dynamic" model, requiring ongoing risk management and transparency throughout the operational lifecycle of an AI system. The challenge is that these two systems often fail to intersect clearly on the timeline of continuous model updates or performance drift.

This gap creates uncertainty: every time a model is updated or exhibits drift, the rules governing the extent of required re-evaluation remain ambiguous, and the responsibility for drawing that boundary is unclear.

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Compounding Factor: GDPR and Data Flow

The operational complexity is further compounded by the GDPR:

• Data Constraints: GDPR's restrictions on the use of personal data limit the ability of AI models to absorb diverse patient cases and improve their diagnostic or predictive accuracy.
• Disrupted Learning: The data procedures mandated by GDPR often interrupt the continuous learning flow required for high-performance AI systems.

The net result is a paradox: the more meticulously the regulatory frameworks are applied to ensure protection, the heavier the innovation burden becomes, often halting the AI's intended self-improvement cycle.

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The Practical Implementation Gap

In practice, the discrepancy between the MDR’s requirement for "fixed specifications" and the AI Act's assumption of a "continuously learning algorithm" leads to misalignment in documentation and scope of assessment.

• Review Delays: Disagreements arise in classifying updates, leading to delays in documentation and the scope of assessment by Notified Bodies.
• Innovation Brake: The EU possesses one of the world's most rigorous legal environments, yet this rigor frequently pauses the necessary cycles of self-correction and improvement inherent in advanced AI technology.

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The Path Forward: A Life-Cycle Approach

Bridging this "static" and "dynamic" gap requires moving beyond the single-instance review model to a framework that supports evaluation across the entire life cycle. It is logical for the EU to prepare new conformity assessments that explicitly account for re-training and model updates.

Ultimately, the divergence point for the industry will be whether regulators and manufacturers continue to treat AI as a "finished product" or begin to treat it as a "continuously evolving system."