Catalina González Martín is an International Project Manager at Dedalus. With a background in biomedical engineering, she now manages several projects focused on the design and integration of software solutions to aid the digitalization of healthcare systems worldwide.
As part of our new series of interviews with GenoMed4All partners, Catalina reflects on the role she’s had during the project, her contributions and main learnings, and her expectations for the project’s impact after it’s officially completed in June 2025.
Involvement across work packages and interdisciplinary collaborations
Dedalus has been working mostly across two work packages (WPs): as a leader for WP5 and as a collaborator on WP4.
These two were closely related and required direct collaboration with some of our partners, especially Humanitas Research Hospital and Universidad Politecnica de Madrid (UPM), and of course all the rest of partners that have been providing the data — which has been crucial for the success of both work packages.
Our main work has been related to the development, the deployment and the validation of the federated learning platform and the homogenization platform. These two platforms serve as the foundation for the implementation, the anonymization and the training of the data models and the algorithms that have been developed in the other WPs that are part of GenoMed4All.
We have also worked very extensively in the treatment of the different data that the hospitals and the other clinical partners have been providing throughout the project, to make sure it could be integrated with the platform while following careful procedures to standardize and anonymize the data.
Impact on precision medicine for hematology
Both work packages have significantly helped the advancement of the federated learning platform, whose ultimate goal is to enhance personalized medicine strategies for hematological diseases. For example, the anonymization of the data is crucial to safeguard the patient’s privacy and for the clinical partners that have provided this data to feed the algorithms.
Even though we have already seen some positive results before the end of the project, I believe the true impact of our work will likely be perceived over time, especially if the platform continues to be trained with new data, or with new models being implemented.
Main takeaways from this 4-year journey
GenoMed4All has been one of the largest and most heterogeneous projects I’ve been involved in. I have certainly learned a lot, both technically and functionally.
As a Project Manager, for me it’s all about organization and coordination. In such a large project as this one, I have learned that your greatest strength can sometimes also be your greatest weakness if you don’t manage things correctly. So I have realized that when you have many different partners from many different countries, languages, background, different professional profiles, the biggest challenge is to know how to manage all this potential together
The main highlight for me has been taking on this challenge, and I think that we have managed to overcome it and to get the best out of each collaborator.
The legacy of GenoMed4All going forward
Through the collaboration and the connection of all the partners in Europe, in GenoMed4All we have created a very powerful tool, and it’s really important to take care of this legacy. Scientifically and functionally, we’ve brought together so many different expertises, and I believe the involvement of such a diverse group of professionals has enriched the evolution of the project from the very beginning.
Also, thinking about the formation of the algorithms and the joint work we’ve done to create the platform, makes this a tool with great potential, one that can be further explored and tested even after the project has finished. So I definitely would like to see that it continues being implemented in the hospital nodes where it has already been deployed, and they continue training it with different new data models, different algorithms and taking advantage of all its full potential.