  Clinical decision support systems (CDSS) augmented with artificial
intelligence (AI) models are emerging as potentially valuable tools in
healthcare. Despite their promise, the development and implementation of these
systems typically encounter several barriers, hindering the potential for
widespread adoption. Here we present a case study of a recently developed
AI-CDSS, Aifred Health, aimed at supporting the selection and management of
treatment in major depressive disorder. We consider both the principles
espoused during development and testing of this AI-CDSS, as well as the
practical solutions developed to facilitate implementation. We also propose
recommendations to consider throughout the building, validation, training, and
implementation process of an AI-CDSS. These recommendations include:
identifying the key problem, selecting the type of machine learning approach
based on this problem, determining the type of data required, determining the
format required for a CDSS to provide clinical utility, gathering physician and
patient feedback, and validating the tool across multiple settings. Finally, we
explore the potential benefits of widespread adoption of these systems, while
balancing these against implementation challenges such as ensuring systems do
not disrupt the clinical workflow, and designing systems in a manner that
engenders trust on the part of end users.
