Innovation at the service of digital health
We leverage artificial intelligence to boost stroke prevention
Experts in supercomputing and artificial intelligence: committed to health
At Innostroke, we believe in prevention as a key measure to combat stroke. This project was born with this vision from the Barcelona Supercomputing Center (BSC) through the Computer Sciences – Workflows and Distributed Computing and Life Sciences – Machine Learning for Biomedical Research groups.
We specialize in Digital Health and Personalized Medicine (eHealth), leveraging emerging technologies and digital tools to enhance medical care, making it more efficient, precise, and tailored to individual needs. Our work focuses on three key pillars within this field:

Our goals
The Innostroke project seeks to establish a commercially viable and scalable business model. To achieve this, it focuses on three main objectives:

Validation and operational deployment of the AI platform
We will conduct a proof of concept in collaboration with the Hospital de la Santa Creu i Sant Pau and Hospital Universitari de Bellvitgel. This process will enable us to refine and optimize the SaaS platform (Software as a Service) based on the results, paving the way for its strategic market introduction.

Regulatory and ethical compliance
We are committed to complying with current regulatory and ethical standards regarding the EU Medical Devices Regulation and AI Act. These are key to healthcare innovations, thus ensuring continuous improvement of project outcomes. In addition, we prioritize cybersecurity in line with the requirements set by the ISO standard.


Roadmap for the creation of a spin-off
In order to create the OneCareAI spin-off from BSC, we are developing the legal documentation and a strategic business plan that will allow us to effectively transfer the technology and successfully position it in the market.
Partners
We will validate the Innostroke technology through a proof of concept (PoC) in collaboration with partner hospitals, evaluating its economic viability and effectiveness in a relevant environment through two case studies as a basis.