# Continuous Design Control for Machine Learning in Certified Medical Systems

arXiv: https://arxiv.org/abs/2209.05843 · HTML: https://mlsanity.com/paper/2209.05843/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 43 references that are in the map.
- First posted on arXiv 13 Sep 2022.

## Builds on (most cited first, 4 of 4)

- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [The Mythos of Model Interpretability](https://mlsanity.com/paper/1606.03490/) (2016)
- [On Medical Device Cybersecurity Compliance in EU](https://mlsanity.com/paper/2103.06809/) (2021)
- [Introducing Traceability in GitHub for Medical Software Development](https://mlsanity.com/paper/2110.13034/) (2021)

## Abstract (arXiv)

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.