Since its origins, medicine has been more linked to the cure of diseases than to their prevention. This is due to multiple factors, training of health professionals aimed at curing diseases, lack of quality data, processing capacity, poor multidisciplinary approach, etc. However, this paradigm is changing, focusing on maintaining the health of individuals to avoid diseases, improving social welfare. To achieve this, the new approach proposes that medicine must be Preventive, Participatory, Predictive, and Personalized (P4 Medicine). In this chapter, we will analyze how artificial intelligence can convincingly contribute to the construction of P4 Medicine, through the processing of key data such as DNA, electronic medical records and environmental variables to which people have been exposed. Here we can find complex data such as Computed Tomography images, electroencephalograms, free text in electronic medical records, pharmacological data, etc. These data have grown exponentially and efforts to improve their quality are already paying off. However, it is no longer possible for a health professional to analyze them to provide a better diagnosis or carry out preventive work on diseases, requiring the formation of multidisciplinary teams to find new solutions to ancient problems, such as healthcare, where data processing, knowledge extraction and its subsequent parameterization in support systems for medical decision-making are vital to save lives. In this sense, artificial intelligence, together with new methods for processing complex data and computational resources to process massive data, will be key to improving the humanity health.