Fully automatic deep learning has become the state-of-the-art technique for many tasks including image acquisition, analysis andinterpretation, and for the extraction of clinically useful information for computer-aided detection, diagnosis, treatment planning,intervention and therapy. However, the unique challenges posed by medical image analysis suggest that retaining a human end-user in any deep learning enabled system will be beneficial. In this review we investigate the role that humans might play in thedevelopment and deployment of deep learning enabled diagnostic applications and focus on techniques that will retain a significantinput from a human end user. Human-in-the-Loop computing is an area that we see as increasingly important in future research dueto the safety-critical nature of working in the medical domain. We evaluate four key areas that we consider vital for deep learningin the clinical practice: (1)Active Learningto choose the best data to annotate for optimal model performance; (2)Interaction withmodel outputs- using iterative feedback to steer models to optima for a given prediction and offering meaningful ways to interpretand respond to predictions; (3) Practical considerations- developing full scale applications and the key considerations that need tobe made before deployment; (4)Future Prospective and Unanswered Questions- knowledge gaps and related research fields thatwill benefit human-in-the-loop computing as they evolve. We offer our opinions on the most promising directions of research andhow various aspects of each area might be unified towards common goals.