A computer-implemented method for identifying prior medical studies of a patient, comprising: receiving a request for relevant prior studies for a patient from a practitioner device utilized by a medical professional, the request including a current study for the patient, the relevant prior studies being relevant to the current study; determining the relevant prior studies from prior studies of the patient based on a personalized model, the personalized model associated with the medical professional, the personalized model indicating a relevance score of the relevant prior studies to the current study, wherein the personalized model is based on a base model and feedback data, wherein the base model is created based on the prior studies and further prior studies for further patients; and receiving the prior studies and the further prior studies, sorting the prior studies and further prior studies; generating a list of pairs of the prior studies and the further prior studies; and determining a ground truth label indicative of whether each of the pairs is one of a relevant pair and an irrelevant pair; transmitting the relevant prior studies to the practitioner device; receiving a report from the practitioner device for the current study, the report including a recommendation for a further procedure to be performed on the patient; determining further relevant prior studies from the prior studies of the patient based on the base model; determining whether the further relevant prior studies negate a need for the recommendation; and when the need for the recommendation is negated, updating the report to remove the recommendation. receiving a request for relevant prior studies for a patient from a practitioner device utilized by a medical professional, the request including a current study for the patient, the relevant prior studies being relevant to the current study; determining the relevant prior studies from prior studies of the patient based on a personalized model, the personalized model associated with the medical professional, the personalized model indicating a relevance score of the relevant prior studies to the current study, wherein the personalized model is based on a base model and feedback data, wherein the base model is created based on the prior studies and further prior studies for further patients; and receiving the prior studies and the further prior studies, sorting the prior studies and further prior studies; generating a list of pairs of the prior studies and the further prior studies; and determining a ground truth label indicative of whether each of the pairs is one of a relevant pair and an irrelevant pair; transmitting the relevant prior studies to the practitioner device; receiving a report from the practitioner device for the current study, the report including a recommendation for a further procedure to be performed on the patient; determining further relevant prior studies from the prior studies of the patient based on the base model; determining whether the further relevant prior studies negate a need for the recommendation; and when the need for the recommendation is negated, updating the report to remove the recommendation.