Model Development & Optimization
We support development of AI/ML models for a range of clinical and diagnostic applications, including signal and image processing, predictive algorithms, and classification systems. Our team works with client-provided data or helps define protocols for clinical data collection, cleaning, annotation, and validation. We optimize models for performance, interpretability, and deployment within embedded or cloud-based medical systems.
AI & Machine Learning
Algorithm Integration into Devices
We bring AI from the lab to the field by embedding machine learning algorithms directly into connected devices, edge processors, and SaMD platforms. Whether deployed on-device or in the cloud, we ensure the model architecture and execution environment meet performance, safety, and latency requirements for real time medical applications.
Regulatory Strategy for AI/ML Devices
Navigating the evolving regulatory landscape for AI/ML in healthcare requires specialized knowledge. We help clients understand and comply with FDA expectations for Good Machine Learning Practice (GMLP), SaMD risk classification, and change control frameworks. Our team supports the preparation of documentation for FDA submissions (e.g. 510(k), De Novo), including model description, training data sources, performance validation, labeling, and update protocols.
Model Validation & Performance Testing
We design and execute rigorous test protocols to demonstrate model performance across clinically relevant conditions and patient populations. Our validation workflows align with FDA guidance on locked vs. adaptive models, and include statistical analysis, bias assessment, and reproducibility evaluation.
Risk Management
For AI/ML models used in high-stakes clinical decision making, we help ensure interpretability, traceability, and integration with broader system risk management. Our approach considers the entire product architecture and includes tools for monitoring performance drift, enabling users to understand model outputs, and documenting rationale in compliance with ISO 14971 and IEC 62304.