A webinar about the concepts of trust and trustworthiness in AI for its implementation in brain stroke research. These concepts are central to ensuring seamless integration of AI into healthcare practice. In this context, the critical subject of data harmonization and analysis in brain stroke research, highlighting the importance of reliable and trustworthy resources. Data harmonization, in a broad sense, involves the systematic process of bringing together diverse datasets from multiple sources and ensuring their compatibility and comparability through standardization, normalization, and integration techniques. The successful implementation of data harmonization hinges on the utilization of reliable and trustworthy resources, which are essential for maintaining data integrity, ensuring accuracy, and upholding ethical standards throughout the process. Brain stroke, a leading cause of disability and mortality worldwide, presents a complex landscape of data that demands precise harmonization and analysis to yield meaningful insights. We explored the challenges and opportunities in collating and harmonizing diverse datasets, including clinical, imaging, and genomic data, to facilitate comprehensive stroke research.


Participants: Dr. Dragi Kocev and Dr. Pance Panov from Jozef Stefan Institute, Slovenia | Dr. Carlos Molina, Head of the Stroke Unit, Vall d’Hebron University Hospital. Head of Stroke Group at VHIR