Regulatory readiness isn’t knowing what the rules are today. It’s the institutional muscle to respond when they change tomorrow.
THRIVE Regulatory assessment evaluates your compliance posture across approval pathways, documentation governance, post-market surveillance, lifecycle change management, and patient consent architecture — identifying gaps before regulators find them.
Core capabilities
Approval Pathway Awareness
Understanding of 510(k), De Novo, PMA, and CE mark pathways and their specific implications for AI systems in clinical use.
Risk Classification
Capacity to correctly classify AI medical devices by risk level and apply the appropriate scrutiny.
Intended Use Clarity
Precision of definition of AI system scope, contraindications, target population, and intended clinical environment.
Documentation Governance
Version control, change management, and audit-trail infrastructure for ongoing regulatory compliance.
Post-Market Surveillance & PCCP
Compliance with predetermined change control plans, mandatory incident reporting, and surveillance posture across the AI lifecycle.
Lifecycle Change Governance
Policies governing algorithm updates, model retraining, and scope changes throughout the AI product lifecycle.
Patient Autonomy & Consent Architecture
Institutional capacity to inform patients when AI contributes to clinical decisions and to obtain meaningful consent.
Independent Review Capability
Capacity to subject AI systems to external review, validation, or audit independent of the vendor.
Privacy Compliance
MOVEDHIPAA, GDPR, and patient consent architecture governing collection, storage, processing, and use of healthcare data in AI pipelines.
How Regulatory connects
Regulatory readiness asks ‘are we compliant?’ — Evaluation readiness asks ‘are we actually checking?’. Post-market surveillance obligations (R) require monitoring capabilities (E).
Regulatory classification determines what evidence the institution is obligated to demand at the Technical level.
Documentation governance (R) and procurement lifecycle readiness (V) must align — contracts must reflect regulatory obligations.
What the literature says
“The clinical deployment of AI systems requires an adequate regulatory framework and highly educated, well-trained health professionals to ensure safe, ethical and beneficial use of such systems.”
— IAEA PC9134 (2025)
“The proliferation of AI could lead to the delivery of healthcare services in unregulated contexts and by unregulated providers, which might create challenges for government oversight of health care.”
— WHO, Ethics & Governance of AI for Health (2021)
“Good software engineering practices, data quality assurance, data management, and robust cybersecurity practices — these include methodical risk management and design process that can appropriately capture and communicate design, implementation, and risk management decisions.”
— FDA, Good Machine Learning Practice (2021)