Interest in the application of machine learning (ML) techniques to medicine is growing fast and wide because of their ability to endow decision support systems with so-called artificial intelligence, particularly in those medical disciplines that extensively rely on digital imaging. Nonetheless, achieving a pragmatic and ecological validation of medical AI systems in real-world settings is difficult, even when these systems exhibit very high accuracy in laboratory settings. This difficulty has been called the "last mile of implementation." In this review of the concept, we claim that this metaphorical mile presents two chasms: the hiatus of human trust and the hiatus of machine experience. The former hiatus encompasses all that can hinder the concrete use of AI at the point of care, including availability and usability issues, but also the contradictory phenomena of cognitive ergonomics, such as automation bias (overreliance on technology) and prejudice against the machine (clearly the opposite). The latter hiatus, on the other hand, relates to the production and availability of a sufficient amount of reliable and accurate clinical data that is suitable to be the "experience" with which a machine can be trained. In briefly reviewing the existing literature, we focus on this latter hiatus of the last mile, as it has been largely neglected by both ML developers and doctors. In doing so, we argue that efforts to cross this chasm require data governance practices and a focus on data work, including the practices of data awareness and data hygiene. To address the challenge of bridging the chasms in the last mile of medical AI implementation, we discuss the six main socio-technical challenges that must be overcome in order to build robust bridges and deploy potentially effective AI in real-world clinical settings.