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Model validation

Skill aizech/clinical-skills/.bob/skills/model-validation

A collection of AI agent skills focused on medical imaging and healthcare workflows. Built for radiologists, healthcare IT professionals, and researchers who want AI coding agents to help with imaging workflows, clinical documentation, AI integration, and medical research. Works with Claude Code, Codex, Cursor, Windsurf, and many other agents.

Install
npx -y skills add aizech/clinical-skills --skill model-validation

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Designs and executes validation studies for radiology AI models to ensure clinical reliability and regulatory compliance. Use when user mentions "validate model performance", "external validation", "statistical analysis", "clinical validation", or needs model evaluation.

SKILL.md

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Model Validation Skill

Triggers

  • "validate model performance"
  • "external validation"
  • "statistical analysis"
  • "clinical validation"
  • "model comparison"
  • "regulatory submission"
  • "performance benchmarking"
  • "fairness audit"

Parameters

  • validation_type (required): Type of validation needed
    • internal - Retrospective internal dataset
    • external - Prospective/out-of-distribution testing
    • prospective - Clinical deployment study
    • regulatory - FDA/EMA submission prep
    • fairness - Subgroup disparity analysis
    • comparison - Head-to-head model comparison
  • model_task (required): Model's intended use
    • detection - Sensitivity, specificity, PPV, NPV
    • segmentation - Dice, IoU, Hausdorff distance
    • classification - Accuracy, AUC, F1 score
    • regression - MAE, RMSE, correlation
  • modality (optional): Imaging modality
  • regulatory_path (optional): Target clearance pathway

Validation Framework

Performance Metrics

TaskPrimary MetricsSecondary
DetectionSensitivity, Specificity, AUCPPV, NPV, FROC
SegmentationDice, IoUHausdorff, ASD
ClassificationAccuracy, AUC, F1Sensitivity, Specificity
RegressionMAE, RMSECorrelation, Bland-Altman

Statistical Methods

  • Confidence intervals (bootstrap, binominal)
  • Significance testing (McNemar, DeLong for AUC)
  • Power analysis for sample sizing
  • Multiple comparison correction
  • Subgroup interaction testing

Regulatory Standards

  • FDA 510(k) predicate comparison
  • FDA De Novo requirements
  • EU MDR clinical evaluation
  • IMDRF clinical evidence framework
  • ACR-SIIM AI performance standards

Output Format

Returns structured JSON with:

  • Validation protocol and methodology
  • Required sample size with power analysis
  • Statistical test selection and rationale
  • Results template with standard metrics
  • Interpretation guidelines
  • Regulatory compliance checklist

Usage Examples

validation_type: external
model_task: detection
modality: CT

validation_type: regulatory
model_task: classification
regulatory_path: 510k

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