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Medical care, and particularly the fight against cancer, can be transformed by the Big Data revolution and artificial intelligence. Major hospitals around the world play a major role in this transformation since they bring together those databases that are the richest sources of data and the closest to the clinical reality. The basic method in the field of diagnosis and prediction of response to treatment, as well as better determining of the risk of recidivism, consists of using machine learning and deep learning technologies for the automatic analysis of texts, of images, and of sequencing data. Does this enable us to extract usable data capable of improving our understanding? First, a series of problems needs to be addressed: legal issues such as access to data and their ownership; privacy and patient consent issues; and technical issues such as the interoperability, integration, and quality of data sources. Then, the questions of the evaluation of these aids in decision-making, and their adoption by the medical world can be addressed.