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This paper presents a set of technologies developed to exploit Grid infrastructures for breast cancer CAD, that include (1) federated repositories of mammography images and clinical data over Grid storage, (2) a workstation for mammography image analysis and diagnosis and (3) a framework for data analysis and training machine learning classifiers over Grid computing power specially tuned for medical image based data. An experimental mammography digital repository of approximately 300 mammograms from the MIAS database was created and classifiers were built achieving a 0.85 average area under the ROC curve in a dataset of 100 selected mammograms with representative pathological lesions and normal cases. Similar results were achieved with classifiers built for the UCI Breast Cancer Wisconsin dataset (699 features vectors). Now these technologies are being validated in a real medical environment at the Faculty of Medicine in Porto University after a process of integrating the tools within the clinicians workflows and IT systems.