  In recent years, the number of machine learning (ML) technologies gaining
regulatory approval for healthcare has increased significantly allowing them to
be placed on the market. However, the regulatory frameworks applied to them
were originally devised for traditional software, which has largely rule-based
behaviour, compared to the data-driven and learnt behaviour of ML. As the
frameworks are in the process of reformation, there is a need to proactively
assure the safety of ML to prevent patient safety being compromised. The
Assurance of Machine Learning for use in Autonomous Systems (AMLAS) methodology
was developed by the Assuring Autonomy International Programme based on
well-established concepts in system safety. This review has appraised the
methodology by consulting ML manufacturers to understand if it converges or
diverges from their current safety assurance practices, whether there are gaps
and limitations in its structure and if it is fit for purpose when applied to
the healthcare domain. Through this work we offer the view that there is clear
utility for AMLAS as a safety assurance methodology when applied to healthcare
machine learning technologies, although development of healthcare specific
supplementary guidance would benefit those implementing the methodology.
