FDA-Grade AI Compliance in Medical Device Software
Automate compliance work, understand change impact instantly, and release your AI/ML products faster, without sacrificing quality.
For many teams, internal processes and tools are the main limiting factors to releasing more frequently. Implementing AI and compliance requires medical device software teams to overcome this challenge.
AI Regulatory Compliance
To use AI/ML in medical devices, medical device software teams need to release faster while staying compliant.
Advanced AI and ML models can improve the accuracy and reliability of medical devices. But how do you integrate your AI/ML model into your existing software system and design controls? Thanks to the recent PCCP guidance, medical device software teams are no longer constrained by the FDA when it comes to AI/ML.
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How Heartflow Reuses Components to Build AI SaMD Faster
Heartflow, a SaMD company serving 250,000 patients annually, needed to speed up development and release their AI-based software more frequently. By adopting a system of systems approach, Heartflow transformed their monolithic architecture into a modular, microservice-based system within 10 weeks. Ketryx facilitated the migration of thousands of artifacts, streamlining their architecture and enabling efficient code reuse, reducing complexity, and accelerating release cycles.
Enable your developers to work in their preferred tools, automate compliance work with AI, and release AI/ML software more frequently.
Scale your machine learning models to real-world demands, without scaling your documentation burden.
Rapidly accelerate AI compliance in software development and deployment, while monitoring model drift
Enable your PCCP with control of AI/ML subsystems so you can get to market faster.
Enforcement
Stay compliant with your PCCP and release faster.
Keep your machine learning model compliant with relevant FDA and MDR regulatory requirements and standards. Ensure the ethical, responsible, and effective development and deployment of machine learning models in medical device software.
Risk Management
Reduce the complexity of risk control and validation in AI systems.
FDA guidance requires that any changes made to AI systems and subsystems (AI-DSF) go through rigorous testing, since these changes have cascading impacts.
Learn more about risk controls in AI/ML
Traceability
Establish traceability for DataOps and MLOps.
Enable state-of-the-art AI solutions while all work is documented automatically.
AI Governance
Innovate and scale faster without sacrificing quality through better AI governance.
Built-in enforcement gives your AI Governance Committee or CoE transparency and control.