A machine learning system for classifying medical images based on a presence or absence of a medical condition, the system comprising: a computer memory storing a first corpus of free-text medical reports each of which having an associated medical image and one or more ground truth structured labels assigned by a medical expert; a computer vision model trained to assign a further structured label to an input medical image, the computer vision model including a feature extractor configured to generate a vector of extracted image features for the medical image (304, 906) and an image classifier configured to generate the further structured label using the vector of extracted features; a natural language processor configured to receive as input a free-text medical report and a vector of extracted image features generated by the feature extractor of the computer vision model and to generate a predicted structured label, wherein the natural language processor is trained, using the first corpus of free-text medical reports and associated ground truth structured labels, to use the vectors of extracted image features generated by the feature extractor for the medical images associated with the free-text medical reports in addition to the free-text medical reports to generate the predicted structured labels, the system further comprising: a computer memory storing a second corpus (300, 902) of free-text medical reports (302, 904) that are associated with medical images (304, 906) and wherein the natural language processor is applied to such second corpus of free-text medical reports (302, 904) and responsively generates predicted structured labels (306, 920) for the associated medical images (304, 906), wherein the computer vision model is trained on the medical images (304, 906) and the predicted structured labels (306, 920) generated by the natural language processor for the free-text medical reports of the second corpus to use the feature extractor and the image classifier to generate a further structured label for a further input medical image, wherein the ground truth, further and predicted structured labels (106, 306, 920) comprise labels for presence or absence of a medical condition. a computer memory storing a first corpus of free-text medical reports each of which having an associated medical image and one or more ground truth structured labels assigned by a medical expert; a computer vision model trained to assign a further structured label to an input medical image, the computer vision model including a feature extractor configured to generate a vector of extracted image features for the medical image (304, 906) and an image classifier configured to generate the further structured label using the vector of extracted features; a natural language processor configured to receive as input a free-text medical report and a vector of extracted image features generated by the feature extractor of the computer vision model and to generate a predicted structured label, wherein the natural language processor is trained, using the first corpus of free-text medical reports and associated ground truth structured labels, to use the vectors of extracted image features generated by the feature extractor for the medical images associated with the free-text medical reports in addition to the free-text medical reports to generate the predicted structured labels, a computer memory storing a second corpus (300, 902) of free-text medical reports (302, 904) that are associated with medical images (304, 906) and wherein the natural language processor is applied to such second corpus of free-text medical reports (302, 904) and responsively generates predicted structured labels (306, 920) for the associated medical images (304, 906), wherein the computer vision model is trained on the medical images (304, 906) and the predicted structured labels (306, 920) generated by the natural language processor for the free-text medical reports of the second corpus to use the feature extractor and the image classifier to generate a further structured label for a further input medical image, wherein the ground truth, further and predicted structured labels (106, 306, 920) comprise labels for presence or absence of a medical condition.