Predictive and preventive medicine, as well as the analytics to identify biomarkers and population level patterns, need AI system to be integrated with decision support and management tools.

Data protection, integrity and user privacy need solid and secure infrastructures to store and exchange data within the digital health ecosystem.

Telemedicine, remote patient monitoring and wearable devices need hardware support in sensor and network development.

Size and heterogeneity of data make digital health a data intensive field that needs to adopt big data technologies.

Digital health can integrate assistive technologies to make a huge difference with respect to current standards of care.

Telemedicine can be enhanced by developing smart environments for remote patient monitoring. The data collected can be used to stratify people according to similar conditions and lifestyles and foster healthy ageing.

Bioinformatics technologies help identifying markers that transform curative medicine into predictive and preventive medicine, and designing better therapies (drugs, nutrients and lifestyle).

 An infrastructure is needed to exchange and elaborate data smoothly and transparently and also to enable effective education and communication.

Digital health is data intensive and relies on modeling, analytics and simulation thus requiring significant computing power.

Digital skills of all the stakeholders (including patients) must be increased to succeed in digitizing healthcare system.

 The handling of health and personal data has legal and ethics aspects. The digitation of the healthcare system imposes a change on policy making with digital technologies and a cultural change.

We must attract young people starting with the right dissemination at high school level to constantly fuel the field with talents. Seminars on the impacts that digital health can have and show cases associated with the technologies can inspire.