---
name: aiml-validation-framework-1262b95b9da9
description: AI/ML medical device validation skill implementing FDA's GMLP principles
allowed-tools:
  - Read
  - Write
  - Glob
  - Grep
  - Edit
  - Bash
metadata:
  specialization: biomedical-engineering
  domain: science
  category: Medical Device Software
  skill-id: BME-SK-021
graph:
  domains: [domain:biomedical-engineering]
  skillAreas: [skill-area:machine-learning-frameworks, skill-area:statistical-analysis, skill-area:compliance-automation]
  workflows: [workflow:experiment-design, workflow:peer-review-cycle]
  roles: [role:biomedical-engineer, role:ml-engineer]
---

# 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

```yaml
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


---
Source attribution: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Mega AI Hub.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Mega AI Hub resource metadata

Creator / publisher: a5c-ai
Original project: https://github.com/a5c-ai/babysitter
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Development & Engineering, Healthcare & Life Sciences, Product Management
Roles: Business Intelligence Analyst, Clinical Research, Data Scientist, ML / AI Engineer, Product Manager
Specialties: Specialty not specified
Tools: Fairness Toolkits, Statistical Analysis Tools
Country applicability: United States
Classification basis: Source catalog labels
Instruction SHA-256: 6151c64512f37ce2903bfe00d11bd89aee92303a3cbdab5f15310edebdb6e93f