  Magnetic Resonance Imaging (MRI) of the brain has benefited from deep
learning (DL) to alleviate the burden on radiologists and MR technologists, and
improve throughput. The easy accessibility of DL tools have resulted in the
rapid increase of DL models and subsequent peer-reviewed publications. However,
the rate of deployment in clinical settings is low. Therefore, this review
attempts to bring together the ideas from data collection to deployment into
the clinic building on the guidelines and principles that accreditation
agencies have espoused. We introduce the need for and the role of DL to deliver
accessible MRI. This is followed by a brief review of DL examples in the
context of neuropathologies. Based on these studies and others, we collate the
prerequisites to develop and deploy DL models for brain MRI. We then delve into
the guiding principles to practice good machine learning practices in the
context of neuroimaging with a focus on explainability. A checklist based on
the FDA's good machine learning practices is provided as a summary of these
guidelines. Finally, we review the current challenges and future opportunities
in DL for brain MRI.
