Continuous Design Control for Machine Learning in Certified Medical Systems

TL;DR

This paper proposes a method using pull requests as design controls for continuous development of machine learning in certified medical systems, leveraging model cards for explainability and regulatory auditing.

Contribution

It introduces a novel approach integrating pull requests and model cards to enable continuous development while satisfying regulatory requirements in medical systems.

Findings

Successful application in an industrial medical system

Enhanced explainability through model cards

Supports continuous development in regulated environments

Abstract

Continuous software engineering has become commonplace in numerous fields. However, in regulating intensive sectors, where additional concerns needs to be taken into account, it is often considered difficult to apply continuous development approaches, such as devops. In this paper, we present an approach for using pull requests as design controls, and apply this approach to machine learning in certified medical systems leveraging model cards, a novel technique developed to add explainability to machine learning systems, as a regulatory audit trail. The approach is demonstrated with an industrial system that we have used previously to show how medical systems can be developed in a continuous fashion.