aiml-validation-framework

# AI/ML Validation Framework Skill ## Purpose The AI/ML Validation Framework Skill supports validation of AI/ML-enabled medical devices per FDA Good Machine Learning Practice (GMLP) principles, addressing data quality, model performance, and predetermined change control. ## Capabilities - Training data quality assessment - Ground truth labeling validation - Model performance metrics calculation (AUC, sensitivity, specificity) - Subgroup performance analysis - Bias and fairness evaluation - Predetermined change control plan (PCCP) templates - Clinical validation study design - Locked algorithm vs. adaptive documentation - Model explainability documentation - Performance monitoring planning - Real-world performance tracking ## Usage Guidelines ### When to Use - Validating AI/ML algorithms - Assessing training data quality - Planning clinical validation studies - Preparing FDA AI/ML submissions ### Prerequisites - Algorithm development complete - Training/test datasets curated - Ground truth established - Intended use clearly defined ### Best Practices - Document data management practices - Validate on diverse populations - Plan for performance monitoring - Consider predetermined change control ## Process Integration This skill integrates with the following processes: - AI/ML Medical Device Development - Software Verification and Validation - Clinical Evaluation Report Development - Post-Market Surveillance System Implementation ## Dependencies - FDA AI/ML guidance -...