Abstract: An example computer-implemented method for identifying a heart condition in a non-human subject includes receiving a medical image of the non-human subject, determining by a processor executing a first machine-learning logic and based on the medical image a dimensional feature of a heart of the non-human subject, displaying the dimensional feature of the heart on a graphical user interface, receiving medical information associated with the non-human subject on the graphical user interface, determining by the processor executing a second machine-learning logic and based on the medical information and the dimensional feature of the heart a likelihood of a heart disease, and displaying the likelihood of the heart disease on the graphical user interface.

Abstract: A method includes receiving, by a computing device, one or more medical images of a non-human patient. The method includes providing the one or more medical images to a machine learning model. The method includes receiving, from the machine learning model, a medical interpretation associated with the one or more medical images. The medical interpretation is based on the machine learning model (i) identifying a medical condition based on the one or more medical images and (ii) determining a confidence score that the medical condition is applicable to the non-human patient. The method includes transmitting the one or more medical images and the medical interpretation to a second computing device. At least one of the confidence score threshold or machine learning logic associated with the machine learning model is updated based on feedback from the second computing.

Abstract: A method includes receiving, by a computing device, a patient record and providing the patient record to a machine learning model. The method includes receiving, from the machine learning model, a dynamic prioritization indicator associated with the patient record. The dynamic prioritization indicator is based on the machine learning model identifying a medical condition associated with a non-human patient associated with the patient record. The method also includes comparing the dynamic prioritization indicator associated with the patient record to one or more additional dynamic prioritization indicators associated with one or more additional patient records in a veterinarian radiology worklist to determine an initial prioritization position of the patient record. The method includes, based on the comparison, generating an updated veterinarian radiology worklist. The method also includes transmitting instructions that cause the updated veterinarian radiology worklist to be displayed on a computing device.

Abstract: An example computer-implemented method for identifying a heart condition in a non-human subject includes receiving a medical image of the non-human subject, determining by a processor executing a first machine-learning logic and based on the medical image a dimensional feature of a heart of the non-human subject, displaying the dimensional feature of the heart on a graphical user interface, receiving medical information associated with the non-human subject on the graphical user interface, determining by the processor executing a second machine-learning logic and based on the medical information and the dimensional feature of the heart a likelihood of a heart disease, and displaying the likelihood of the heart disease on the graphical user interface.