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aiml-validation-framework

Testing & Quality
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AI/ML medical device validation skill implementing FDA's GMLP principles

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Source SKILL.md: https://github.com/a5c-ai/babysitter/blob/HEAD/library/specializations/domains/science/biomedical-engineering/skills/aiml-validation-framework/SKILL.md

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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
  • GMLP principles
  • Fairness toolkits (AIF360, Fairlearn)
  • Statistical analysis tools
  • Clinical study resources

Configuration

aiml-validation-framework:
  algorithm-types:
    - locked
    - adaptive
    - continuously-learning
  performance-metrics:
    - AUC
    - sensitivity
    - specificity
    - PPV
    - NPV
  subgroup-categories:
    - age
    - sex
    - race
    - disease-severity

Output Artifacts

  • Data management documentation
  • Algorithm description documents
  • Performance reports
  • Bias/fairness assessments
  • PCCP documents
  • Clinical validation protocols
  • Monitoring plans
  • FDA submission sections

Quality Criteria

  • Training data quality documented
  • Ground truth methodology validated
  • Performance meets clinical requirements
  • Subgroup performance acceptable
  • Bias assessments completed
  • PCCP appropriate for algorithm type