In all areas of science, the volume of literature has become overwhelming, and staying current with the bleeding edge of research is increasingly unmanageable. This problem is particularly acute in medicine, where studies are published at a rate of approximately two per minute, and up-to-date information is crucial for decisionmaking processes. Maintaining the evidence base by updating systematic reviews is a costly and time-consuming endeavour, yet there is comparatively little research that tackles this important problem via computational means. We propose a shared task that addresses this challenge in (biomedical) scholarly information access. Specifically, we propose three tasks involved in updating systematic reviews: determining when a review should be updated, retrieving literature for the review update, and classifying whether each piece of retrieved literature should be included in the review. These are difficult problems that have received limited research attention, and improved solutions could have considerable impact.