  Over the past two decades, exponential growth in data availability,
computational power, and newly available modeling techniques has led to an
expansion in interest, investment, and research in Artificial Intelligence (AI)
applications. Ophthalmology is one of many fields that seek to benefit from AI
given the advent of telemedicine screening programs and the use of ancillary
imaging. However, before AI can be widely deployed, further work must be done
to avoid the pitfalls within the AI lifecycle. This review article breaks down
the AI lifecycle into seven steps: data collection; defining the model task;
data pre-processing and labeling; model development; model evaluation and
validation; deployment; and finally, post-deployment evaluation, monitoring,
and system recalibration and delves into the risks for harm at each step and
strategies for mitigating them.
