Helping medical and health services staff to better understand and interpret results obtained by intelligent systems - specially those ones that perform classification and prediction tasks upon medical data - involves endowing such systems with a high explainability and interpretability capabilities.

It is highly advisable to put care on approaches whereby data or information derived from them are combined with each other to produce knowledge for decisions to be made, diagnosis, etc. A correct homogenization and fusion of data will create an important added value and synergistic effect in the effectiveness of any medical process governed by intelligent systems, whereas an inadequate fusion of such data might give rise to counterproductive results.

A continuous and comprehensive training on the use of digital technologies and AI systems must be envisaged as part of the learning curricula of all health-related professionals, both at public and private levels. This will help them making an optimal use of such systems and technologies, turning them into valuable supplementary tools but not totally substituting humans in certain medical practices.

Under the Internet of Things paradigm, smart environment together with mobile device apps that arechighly interconnected, might give rise to effective and efficient responses to infectious agent propagation risks, as well as warning about contaminant agents in the environment at a fine-grained level.

Recommender systems are a promising tool to help individuals acquiring healthier lifestyles in their everyday life based on their motivations, through the generation of personalized recommendations that balance the taste and health needs of citizens.