Name in original language
Estrategia de Inteligencia Artificial del Sistema Nacional de Salud (eIASNS)
Initiative overview
The strategy is based on an initial diagnostic of the situation of artificial intelligence in the National Health System (SNS), which shows that many solutions already exist but are unevenly implemented across regions. It highlights that most initiatives are at the stage of pilot projects, proofs of concept, or early deployments, with differences in technical capacity, regulatory knowledge, and organisational readiness between health services.
To address this, the strategy defines six key operational dimensions for AI adoption: use case identification, infrastructure, data, governance, training, and overall strategy. It emphasises the need to ensure regulatory compliance, access to quality data, validation of algorithms in clinical environments, and their integration into healthcare processes, while also addressing challenges such as scalability, efficiency, and cultural acceptance.
A central element is the establishment of a federated governance model adapted to the structure of the SNS. This includes coordination between the Ministry of Health and the Autonomous Communities, supported by specific structures such as AI offices, coordination mechanisms, and shared tools. These tools include a centralised marketplace of algorithms, registries, and controlled testing environments, which allow validation, monitoring, and safe deployment of AI systems, as well as ensuring compliance with European and national regulations.
The strategy also outlines concrete areas of implementation through different lines of action. These include the use of AI for diagnostic support, predictive modelling, public health monitoring, administrative automation, and patient interaction through digital assistants. It also includes applications for resource planning, early detection of health risks, and optimisation of healthcare infrastructure, with the aim of improving efficiency and supporting healthcare professionals in their daily work.
Finally, the strategy establishes mechanisms to monitor its implementation and impact. It defines indicators to measure progress in areas such as the number of AI tools deployed, the level of professional training, and the use of AI in clinical decision-making and patient care. These indicators are linked to intermediate milestones and long-term objectives, allowing the system to assess progress towards broader adoption and to ensure that the integration of AI delivers measurable improvements in healthcare services.
