An introduction to artificial intelligence applications in medicine: Historical aspects

In a broad sense, artificial intelligence and machine learning have been applied to medical data since the beginning of computing given the deep roots of this area in innovation, but recent years have witnessed an increasing generation of data related to health sciences, an issue that has given birth to a new field of computer science called big data. Large-scale medical data (in the form of structured and unstructured databases) if properly acquired and interpreted can generate great benefits by reducing costs and times of health service, but could also serve to predict epidemics, improve therapeutic schemes, advise doctors in remote places and improve the quality of life. The deep learning algorithms are especially useful to deal with this large amount of complex, poorly documented and generally unstructured data, all this because deep learning can break when creating models that automatically discover the predictive characteristics of a large amount of complex data. In the future, the human-machine relationship in the medical evaluation will be narrower and complex; while the machine would be responsible for extraction, cleaning and assisted searches, the physician will be concentrate on both, data interpretation and the best treatment option, improving the patient ' s attention and ultimately, quality of life.