Silvia Uribe Mayoral is an Associate Professor at Universidad Politécnica de Madrid (UPM), where she’s been involved in various EU-funded projects focused on ICT applied to healthcare innovation, including GenoMed4All and SYNTHEMA among many others. With an academic and professional background in Telecommunications Engineering, she also holds a PhD in Technology and Communication Systems.
As part of our new series of interviews with GenoMed4All partners, Silvia reflects on the role she’s fulfilled during the project, and her expectations for its impact on fostering Federated Learning systems in clinical contexts after the project ends in June 2025.
Work package leadership and interdisciplinary collaborations
At UPM, my team and I have been leading efforts within Work Package 4, where our primary focus has been the development of the Federated Learning Platform. Our goal has been to address one of the key challenges in clinical AI: how to access and utilize sensitive clinical data for algorithm development while respecting the strict privacy requirements inherent in healthcare.
We began by engaging closely with various stakeholders, both from the clinical side and the software development community, to understand their specific needs. Based on this input, we designed and implemented a fully operational federated learning platform.
This platform enables data scientists and other users to develop AI algorithms directly on distributed datasets across different clinical nodes, without the need to centralize the data; in this architecture, the data stays where it is, and only the algorithms travel. This approach significantly enhances data privacy and compliance, especially in a clinical environment where patient confidentiality is paramount.
Impact on precision medicine for hematology
I believe our contribution is particularly important for the advancement of precision medicine in hematology.
By allowing stakeholders to access and analyze distributed clinical data without physically moving it, our platform opens up new possibilities for developing AI-driven diagnostic and decision-support tools. It respects privacy regulations while maximizing the value of data, which is crucial when working with highly sensitive and personal information.
The legacy of GenoMed4All going forward
Reflecting on my experience with GenoMed4All, one key takeaway is that despite all our advancements, there are still many unmet needs within the healthcare system. Technology, especially in the field of AI, has tremendous potential to address these gaps, but it requires a deep understanding of the context and a commitment to designing solutions that are both effective and ethically sound.
Looking ahead, I see the federated learning platform we’ve developed as a strong foundation for future work; it’s already been deployed across several nodes and has demonstrated real functionality. I hope it will serve as a springboard for new initiatives, helping us to tackle further challenges in clinical AI and continue building solutions that meet the evolving needs of healthcare.