Turn AI requirements into defensible evidence.
Assess your AI-enabled medical device across regulation, Design Controls, risk management and the full product lifecycle.
Decision support.
Expert review before implementation.
Define the regulatory context
The same AI function can trigger different obligations depending on its intended purpose, device role and market pathway.
Where does your evidence break?
Change each control status. The readiness score and priority actions update instantly.
Integrate AI controls. Do not build another silo.
US requirements do not sit in one AI regulation. FDA expectations connect the device QMS, Design Controls, software lifecycle, risk management, clinical evidence, Human Factors and post-market controls.
AI role, users, clinical decision, autonomy and foreseeable reliance
RA · Clinical · Human FactorsModel inputs, outputs, operating limits, failure behaviour and interface requirements
Systems · Software · AIProvenance, suitability, representativeness, separation, bias controls and model versioning
AI · Data governance · ClinicalAI hazards, subgroup performance, uncertainty, use-related risk and human–AI team performance
Risk · V&V · Clinical · Human FactorsValidated baseline, limitations, user information, training and deployment controls
QA · RA · Software · Human FactorsDrift metrics, thresholds, escalation, revalidation and PCCP alignment
PMS · AI · CCB · RAKeep the system usable as evidence grows.
Use a short product-specific AI lifecycle plan as the control map. It should point to controlled evidence rather than duplicate it.
- Existing QMS proceduresDesign Controls, software, risk, clinical, Human Factors, PMS and change control
- AI-specific modulesData, model development, bias, drift, transparency and PCCP
- Interface controlsInputs, outputs, ownership, review points and approval gates
- End-to-end traceabilityIntended use through model versions, risks, tests, release and monitoring
Validate the human–AI team, not the model alone.
Clinical evaluation asks whether the device achieves its intended clinical purpose. Human Factors asks whether intended users can interpret the AI output and act safely. Risk management connects interaction failures, automation bias and delayed intervention to hazardous situations and harm.
Regulatory basis: FDA AI-enabled device lifecycle draft guidance · FDA final PCCP guidance · Good Machine Learning Practice
2 critical gaps · 3 partial controls
Your next evidence decisions
Turn the gap list into a controlled implementation plan.
TRA supports MedTech teams at strategic and operational levels—connecting EU and US AI expectations with QMS, Design Controls, risk management, software, data, clinical evidence, Human Factors, validation and post-market controls.