A system , comprising: at least one data processor ; and at least one memory storing instructions which, when executed by the at least one data processor , result in operations comprising: receiving, from a camera at an infusion pump , one or more images of an area of interest of the infusion pump loaded with an infusion set ; identifying, within the one or more images, a first component of the infusion pump and a second component of the infusion set in the area of interest, wherein the first component comprises a bezel, a membrane seal, a door , a platen , a locator feature , or an air-in-line detector , and wherein the second component comprises an upper fitment , a lower fitment , or a pumping segment of a tubing; applying a machine learning model trained to detect one or more nonconformities present in the one or more images of the infusion pump loaded with the infusion set based at least on a relative position of the first component and the second component and by at least comparing, to one or more images of a correctly loaded infusion set , the one or more images of the infusion pump loaded with the infusion set ; and in response to an output of the machine learning model indicating a presence of a nonconformity in the one or more images of the infusion pump loaded with the infusion set , performing a corrective action. at least one data processor ; and at least one memory storing instructions which, when executed by the at least one data processor , result in operations comprising: receiving, from a camera at an infusion pump , one or more images of an area of interest of the infusion pump loaded with an infusion set ; identifying, within the one or more images, a first component of the infusion pump and a second component of the infusion set in the area of interest, wherein the first component comprises a bezel, a membrane seal, a door , a platen , a locator feature , or an air-in-line detector , and wherein the second component comprises an upper fitment , a lower fitment , or a pumping segment of a tubing; applying a machine learning model trained to detect one or more nonconformities present in the one or more images of the infusion pump loaded with the infusion set based at least on a relative position of the first component and the second component and by at least comparing, to one or more images of a correctly loaded infusion set , the one or more images of the infusion pump loaded with the infusion set ; and in response to an output of the machine learning model indicating a presence of a nonconformity in the one or more images of the infusion pump loaded with the infusion set , performing a corrective action. receiving, from a camera at an infusion pump , one or more images of an area of interest of the infusion pump loaded with an infusion set ; identifying, within the one or more images, a first component of the infusion pump and a second component of the infusion set in the area of interest, wherein the first component comprises a bezel, a membrane seal, a door , a platen , a locator feature , or an air-in-line detector , and wherein the second component comprises an upper fitment , a lower fitment , or a pumping segment of a tubing; applying a machine learning model trained to detect one or more nonconformities present in the one or more images of the infusion pump loaded with the infusion set based at least on a relative position of the first component and the second component and by at least comparing, to one or more images of a correctly loaded infusion set , the one or more images of the infusion pump loaded with the infusion set ; and in response to an output of the machine learning model indicating a presence of a nonconformity in the one or more images of the infusion pump loaded with the infusion set , performing a corrective action. <EOS>