Automated ML Model Certification for SaMD Compliance
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Solution Overview
Problem
Current machine learning model retraining in online Software as a Medical Device (SaMD) lacks automation for assessing changes in performance and compliance, requiring manual operations and regulatory approvals, which is time-consuming and inefficient.
Innovation Solution
A method for continuously monitoring and updating machine learning models, automatically certifying changes for compliance with performance characteristics and compliance criteria, including model comparison, validation, and regulatory impact assessment, using an interpretable model comparison service with machine learning components for tracking model versions and clinical context.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning models are continuously updated in online SaMD, then model performance and adaptability are improved, but manual certification and regulatory compliance processes become more complex and time-consuming
Solution Approach 1:
The system performs preliminary actions by establishing compliance criteria and performance characteristics before model updates occur. Validation rules and compliance requirements are predefined, allowing automated assessment of model changes against these pre-established standards, thereby simplifying the certification process for continuous updates
Solution Approach 2:
The system implements self-service through automated model validation and compliance assessment mechanisms. The validation service automatically evaluates model changes against predefined compliance criteria and generates certification determinations without requiring manual regulatory review for each update, enabling continuous deployment while maintaining compliance
2Reliability
If manual operations are used for model retraining and certification, then regulatory compliance can be ensured, but processing time and operational efficiency are reduced
Solution Approach 1:
The system implements feedback mechanisms where model validation results and compliance assessment outcomes are automatically fed back into the deployment process. This closed-loop feedback enables automated decision-making regarding model updates, maintaining compliance assurance while significantly improving update efficiency by eliminating manual review cycles
Solution Approach 2:
The system replaces manual mechanical processes with automated computational processes. Instead of manual review and certification operations, the system uses automated validation services that computationally assess model changes against compliance criteria, thereby maintaining reliability while dramatically improving productivity
3Measurement precision
If comprehensive model validation and compliance assessment are performed, then certification accuracy is improved, but computational resources and processing time are consumed
Solution Approach 1:
The system applies partial action by focusing validation efforts on the specific changes introduced in model updates rather than re-validating the entire model. The validation service assesses compliance criteria selectively based on what has changed, maintaining assessment accuracy while reducing unnecessary computational resource consumption
Data Source
AI summary
Machine learning model change management in an online Software as a Medical Device (“SaMD”) is provided. One or more machine learning models implemented in a medical domain may be monitored where the one or more machine learning models are continuously updated. One or more changes to the one or more machine learning models. The one or more machine learning models, having the one or more changes, are certified as being in compliance with performance characteristics and compliance criteria.