Big data and machine learning in health

Introduction Big data is defined as the amount of data that once organized and analysed, can make a value, make decisions, make predictions and discover patterns in order to reduce costs, avoid risks and optimize services. Machine Learning (ML) is a field of artificial intelligence and is characterized as a method of machine learning, which uses algorithms that learn from data analysis, allowing computers to find patterns, draw conclusions and make predictions. These tools can be used in different areas of human knowledge, particularly in the health sector which are generated daily a huge amount of information, allowing the creation of algorithms that learn and gain understanding to assist in various clinical practices. Objectives The purpose of this paper is to analyse the benefits of Big Data and Machine Learning in providing overall health care. Methodology We conducted a review of the scientific literature published in the electronic databases PubMed/MEDLINE and Google Scholar, according to specific criteria, using keywords: Big Data”, “Machine Learning". Results In the field of oncology (skin cancer, breast, lung, leukaemia) ML and Big Data have contributed to early diagnosis of different pathologies and their evolution, as well as optimizing therapies. In ophthalmology (diabetic retinopathy and congenital cataract) has shown high efficacy in rapid diagnosis and appropriate treatment crucial to prevent the progress of the disease. The tested algorithms achieved very favourable results in cases of Parkinson’s and cardiovascular diseases. In the pharmaceutical industry these computer and digital tools have contributed to the optimization of clinical trials, genome sequencing of tumours to then identify and develop specific drugs to fight it. Conclusion Advances of MIL and Big data are notorious and development opportunities are immense and can come to revolutionize tasks such as diagnosis, treatment and health care in general.