AI Compliance for Healthcare
Healthcare AI systems are classified as high-risk under the EU AI Act. From diagnostic imaging to clinical decision support, your AI must meet rigorous safety, transparency, and governance requirements.
EU AI Act Risk Classification for Healthcare
Medical Devices & Diagnostics
AI systems intended for medical diagnosis, prognosis, or treatment recommendations.
Examples in Healthcare:
- •AI-powered radiology analysis
- •Pathology slide interpretation
- •Symptom checkers recommending treatment
- •Predictive patient deterioration alerts
Clinical Decision Support
Systems that influence clinical decisions about patient care.
Examples in Healthcare:
- •Drug interaction checkers
- •Treatment pathway recommendations
- •Risk stratification models
- •Sepsis prediction algorithms
Patient-Facing Chatbots
AI systems interacting with patients requiring transparency disclosures.
Examples in Healthcare:
- •Appointment scheduling bots
- •Symptom triage chatbots
- •Mental health support assistants
- •Medication reminder systems
Administrative AI
Back-office AI with minimal patient impact.
Examples in Healthcare:
- •Medical coding automation
- •Claims processing optimization
- •Supply chain management
- •Staff scheduling algorithms
August 2026 Deadline
Healthcare companies deploying AI in the EU must achieve compliance by August 2026. Start your readiness assessment now to avoid rushing implementation or facing penalties up to 7% of global revenue.
Key Requirements for Healthcare AI
Clinical Validation & Testing
AI systems must be validated against clinical outcomes with representative patient populations. Document sensitivity, specificity, and performance across demographic groups.
Bias Testing Across Demographics
Test and document AI performance across age, sex, ethnicity, and health conditions. Implement ongoing monitoring for performance drift.
Human-in-the-Loop Requirements
Clinical AI must support, not replace, clinician judgment. Design for appropriate human oversight in diagnostic and treatment decisions.
Explainability for Clinicians
Provide clinicians with interpretable outputs explaining AI reasoning. Document model logic and limitations in clinical contexts.
Patient Data Governance
Implement robust data governance for PHI used in AI training and inference. Ensure HIPAA compliance and EU data protection requirements.
Continuous Performance Monitoring
Implement real-time monitoring for model drift, accuracy degradation, and adverse events. Establish incident response protocols.
Implementation Roadmap
Follow this Healthcare-specific roadmap to achieve AI compliance. Most organizations complete these steps in 6-12 months.
Inventory all AI systems touching patient care or clinical workflows
Classify each system under EU AI Act risk tiers and MDR/IVDR where applicable
Conduct clinical validation studies with diverse patient populations
Implement bias testing and demographic performance analysis
Design human oversight workflows for high-risk clinical AI
Establish data governance for PHI in AI pipelines
Document model explainability and clinician-facing disclosures
Create incident response and adverse event reporting processes
Start Your AI Governance Journey
Get a personalized readiness score and action plan for your Healthcare AI systems. Our calculator maps your current state to ISO 42001 and EU AI Act requirements.
Get Free AI Readiness ScoreNo credit card required
Healthcare AI Compliance FAQs
Does the EU AI Act apply if we only serve US patients?
If your AI system is used to make decisions about EU citizens or is marketed in the EU, the AI Act applies. Many US healthcare companies with international operations or EU customers will need to comply.
How does the EU AI Act interact with FDA AI/ML regulations?
They are complementary but distinct. FDA focuses on safety and effectiveness for US market authorization. The EU AI Act adds transparency, bias testing, and governance requirements. Organizations selling in both markets need to satisfy both frameworks.
Is ISO 42001 required for healthcare AI?
Not legally required, but increasingly expected by health systems and payers. ISO 42001 certification demonstrates systematic AI governance and can streamline EU AI Act compliance. Many procurement processes now ask about AI management systems.
What about AI used only for research, not clinical care?
Research-only AI may fall outside high-risk classifications if not used for clinical decisions. However, if research AI informs patient care or transitions to clinical use, it becomes subject to full requirements.
Explore Other AI Compliance Industries
Kevin A
Principal Security & GRC Engineer
Kevin is a security engineer turned GRC specialist. He focuses on mapping cloud-native infrastructure (AWS/Azure/GCP) to modern compliance frameworks, ensuring that security controls are both robust and auditor-ready without slowing down development cycles.
About RiscLens
Our mission is to provide transparency and clarity to early-stage technology companies navigating the complexities of SOC 2 (System and Organization Controls 2) compliance.
Who we serve
Built specifically for early-stage and growing technology companies—SaaS, fintech, and healthcare tech—preparing for their first SOC 2 audit or responding to enterprise customer requirements.
What we provide
Clarity before commitment. We help teams understand realistic cost ranges, timeline expectations, and common gaps before they engage auditors or expensive compliance vendors.
Our Boundaries
We do not provide legal advice, audit services, or certifications. Our assessments support internal planning—they are not a substitute for professional compliance guidance.
SOC 2 (System and Organization Controls 2) is a voluntary compliance standard for service organizations, developed by the AICPA, which specifies how organizations should manage customer data based on the Trust Services Criteria: security, availability, processing integrity, confidentiality, and privacy.
Get your personalized SOC 2 cost estimate
Free • No sales calls • Instant results