  Successful deployment of machine learning algorithms in healthcare requires
careful assessments of their performance and safety. To date, the FDA approves
locked algorithms prior to marketing and requires future updates to undergo
separate premarket reviews. However, this negates a key feature of machine
learning--the ability to learn from a growing dataset and improve over time.
This paper frames the design of an approval policy, which we refer to as an
automatic algorithmic change protocol (aACP), as an online hypothesis testing
problem. As this process has obvious analogy with noninferiority testing of new
drugs, we investigate how repeated testing and adoption of modifications might
lead to gradual deterioration in prediction accuracy, also known as
``biocreep'' in the drug development literature. We consider simple policies
that one might consider but do not necessarily offer any error-rate guarantees,
as well as policies that do provide error-rate control. For the latter, we
define two online error-rates appropriate for this context: Bad Approval Count
(BAC) and Bad Approval and Benchmark Ratios (BABR). We control these rates in
the simple setting of a constant population and data source using policies
aACP-BAC and aACP-BABR, which combine alpha-investing, group-sequential, and
gate-keeping methods. In simulation studies, bio-creep regularly occurred when
using policies with no error-rate guarantees, whereas aACP-BAC and -BABR
controlled the rate of bio-creep without substantially impacting our ability to
approve beneficial modifications.
