  Healthcare is one of the most promising areas for machine learning models to
make a positive impact. However, successful adoption of AI-based systems in
healthcare depends on engaging and educating stakeholders from diverse
backgrounds about the development process of AI models. We present a broadly
accessible overview of the development life cycle of clinical AI models that is
general enough to be adapted to most machine learning projects, and then give
an in-depth case study of the development process of a deep learning based
system to detect aortic aneurysms in Computed Tomography (CT) exams. We hope
other healthcare institutions and clinical practitioners find the insights we
share about the development process useful in informing their own model
development efforts and to increase the likelihood of successful deployment and
integration of AI in healthcare.
