  Many institutions within the healthcare ecosystem are making significant
investments in AI technologies to optimize their business operations at lower
cost with improved patient outcomes. Despite the hype with AI, the full
realization of this potential is seriously hindered by several systemic
problems, including data privacy, security, bias, fairness, and explainability.
In this paper, we propose a novel canonical architecture for the development of
AI models in healthcare that addresses these challenges. This system enables
the creation and management of AI predictive models throughout all the phases
of their life cycle, including data ingestion, model building, and model
promotion in production environments. This paper describes this architecture in
detail, along with a qualitative evaluation of our experience of using it on
real world problems.

Machine Learning;Artificial Intelligence;Computers and Society