We discuss the new paradigm of predictive health intelligence, based on the use of modern deep learning algorithms and big biomedical data, along the various dimensions of: a) its potential, b) the limitations it encounters, and c) the sense it makes. We conclude by reasoning on the idea that viewing data as the unique source of sanitary knowledge, fully abstracting from human medical reasoning, may affect the scientific credibility of health predictions.
Keywords: artificial intelligence; data-fitting; data-interpreting; health predictions; machine and deep learning; predictive health intelligence.