A muggles guide to deep learning wizardry

Growing interest in the applications of artificial intelligence (AI) and, in particular, deep learning (DL) in nuclear medicine and radiology partitions the professional community. At one end of the spectrum are our expert DL wizards developing potion-like code and waving the DL capabilities like a wand across our professions. On the opposite side of the spectrum are our muggle colleagues who lack the wizardry of DL and may be largely oblivious to the entire magical realm.

As crafted by Arthur C Clark, any sufficiently advanced technology is indistinguishable from magic. DL is not only an important technology in the future of medical imaging, but its application lives in the capabilities of medical imaging technologists. This may be incidental through application of techniques at the patient interface, through role expansion in data curation and management, or as active members of DL projects and development. Understanding the rudimentary principles of DL is emerging as requisite in medical imaging.

AI and DL are valuable tools in advancing capabilities and outcomes in medical imaging. A working knowledge of the technology and techniques is important and achievable for the medical imaging technologist even when capability in application of DL to research and clinical practice is not within one's interests or scope of practice.

While there is no requisite for all of the professional community to be tutored in the wizardry of DL, there are benefits for the profession and our patients for all to have a rudimentary understanding of the language and landscape. The breadth of DL literature assumes a level of understanding not evident for the bulk of our professions. This manuscript provides a simplified primer on DL with the aim of arming the muggles among us with sufficient insight to navigate the magical realm of DL without transferring any wizardry capability itself.