An x-ray image laterality detection system, comprising: a detection computing device (202, 800) comprising at least one processor in communication with at least one memory device, wherein said at least one processor is programmed to: receive an unclassified x-ray image, wherein the unclassified x-ray image is acquired with a lead marker; execute a neural network model for analyzing x-ray images in order to analyze the unclassified x-ray image using the neural network model, wherein the neural network model (204, 338, 600, 1600, 2600, 3600) is trained with training x-ray images as inputs and observed laterality classes associated with the training x-ray images as outputs; assign a laterality class to the unclassified x-ray image based on the analysis; if the assigned laterality class is not a target laterality, the at least one processor is programmed to: adjust the unclassified x-ray image by flipping the unclassified x-ray image to derive a corrected x-ray image having the target laterality; and output the corrected x-ray image, wherein the lead marker is digitally blocked out or covered up in the corrected x-ray image; and if the assigned laterality class is the target laterality, the at least one processor is programmed to output the unclassified x-ray image, wherein the target laterality comprises a proper laterality wherein the right side of the x-ray image depicts the left side of a patient. a detection computing device (202, 800) comprising at least one processor in communication with at least one memory device, wherein said at least one processor is programmed to: receive an unclassified x-ray image, wherein the unclassified x-ray image is acquired with a lead marker; execute a neural network model for analyzing x-ray images in order to analyze the unclassified x-ray image using the neural network model, wherein the neural network model (204, 338, 600, 1600, 2600, 3600) is trained with training x-ray images as inputs and observed laterality classes associated with the training x-ray images as outputs; assign a laterality class to the unclassified x-ray image based on the analysis; if the assigned laterality class is not a target laterality, the at least one processor is programmed to: adjust the unclassified x-ray image by flipping the unclassified x-ray image to derive a corrected x-ray image having the target laterality; and output the corrected x-ray image, wherein the lead marker is digitally blocked out or covered up in the corrected x-ray image; and if the assigned laterality class is the target laterality, the at least one processor is programmed to output the unclassified x-ray image, receive an unclassified x-ray image, wherein the unclassified x-ray image is acquired with a lead marker; execute a neural network model for analyzing x-ray images in order to analyze the unclassified x-ray image using the neural network model, wherein the neural network model (204, 338, 600, 1600, 2600, 3600) is trained with training x-ray images as inputs and observed laterality classes associated with the training x-ray images as outputs; assign a laterality class to the unclassified x-ray image based on the analysis; if the assigned laterality class is not a target laterality, the at least one processor is programmed to: adjust the unclassified x-ray image by flipping the unclassified x-ray image to derive a corrected x-ray image having the target laterality; and output the corrected x-ray image, wherein the lead marker is digitally blocked out or covered up in the corrected x-ray image; and adjust the unclassified x-ray image by flipping the unclassified x-ray image to derive a corrected x-ray image having the target laterality; and output the corrected x-ray image, wherein the lead marker is digitally blocked out or covered up in the corrected x-ray image; and if the assigned laterality class is the target laterality, the at least one processor is programmed to output the unclassified x-ray image, wherein the target laterality comprises a proper laterality wherein the right side of the x-ray image depicts the left side of a patient.