The EU AI Act establishes a risk-based framework for trustworthy clinical AI, with obligations that phase in over several years. Providers that act early can align safety, ethics, and performance with legal requirements and avoid costly retrofits. Key milestones include publication in mid-2024, entry into force shortly after, and progressive deadlines culminating around the three-year mark for high-risk obligations.
Inventory use cases and assign accountability
Start with an enterprise-wide inventory of algorithms that influence care or operations: decision support, triage, diagnostics, imaging, scheduling, revenue integrity, and patient engagement tools. Determine whether your organization acts as a “provider” (placing a system on the market or substantially modifying it) or a “deployer” (operational user). Assign accountable owners for clinical safety, data governance, cybersecurity, and compliance, and define measurable outcomes for safety, fairness, and explainability.
Classify risk and confirm scope
For each system, determine risk level. Many clinical applications—especially those that are safety components of medical devices—will fall under high-risk requirements. Conduct a structured assessment that examines intended purpose, affected patient populations, potential harm, and the impact on fundamental rights. Document any prohibited practices and plan decommissioning where necessary.
Align with medical device pathways
If the AI is part of a regulated medical device, integrate compliance work with MDR/IVDR activities. Unify your quality management system, risk management file, clinical evaluation, and post-market surveillance so one evidence set satisfies both regimes. Establish change-control criteria that distinguish routine model updates from substantial modifications that demand new assessments.
Build the technical file and data governance
Create a living technical file that captures intended use, system architecture, data lineage, training and test protocols, model cards, known limitations, and human-factors testing. Implement data governance that verifies representativeness, relevance, and statistical properties; logs dataset provenance; and tracks model versions. Bake in bias detection and mitigation, clear performance thresholds, and security controls across the lifecycle.
Engineer effective human oversight
Design oversight so clinicians can understand model limits, recognize failure modes, override outputs, and escalate concerns. Provide role-specific training, clear instructions for use, calibrated confidence indicators, rationale summaries, and “stop-the-line” controls. Ensure workflows specify when human review is mandatory, how disagreements are resolved, and how patient communication is handled.
Operationalize with policies, testing, and monitoring
Translate principles into practice through policies for procurement, onboarding, change management, incident reporting, and decommissioning. Run pre-deployment simulations and real-world pilots with human-in-the-loop safeguards. Establish continuous monitoring for performance drift, safety signals, fairness metrics, and cybersecurity events, and tie these to clear remediation playbooks and audit trails.
Hit the deadlines without surprises
Map obligations to a multi-year plan: near-term restrictions on prohibited uses, early transparency duties, and staged requirements for providers and deployers of high-risk systems. Build quarterly milestones, align budget and staffing, and prioritize dependencies such as quality management, technical documentation, and clinical evaluation. Use internal audits to validate readiness before each regulatory cutover.
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