Universitat Politécnica de Valéncia
Conceptual Modeling: The Backbone of Federated Learning in Healthcare
28/03/25 11:33
Curiosity has always been behind innovation, especially in healthcare. Think about how far we have come: from stacks of paper-based patient records, scattered across different hospitals, and difficult to share, to an era where AI has the potential to revolutionize medicine. Today, federated learning allows institutions to collaborate on research while preserving patient privacy.
But there’s a challenge: how do we ensure that data from different hospitals, recorded in different formats and languages, can work together?
Without a shared structure, even the most advanced AI models would struggle to make sense of fragmented and inconsistent data. That’s where conceptual modeling becomes the secret ingredient that transforms scattered information into a unified and powerful resource for medical research. By creating a common language, conceptual modeling allows different datasets to “speak” to each other, ensuring that AI can learn from them in a meaningful way.
At the heart of the BETTER project, conceptual modeling plays a crucial role in three key areas: ETL (Extract, Transform, Load) processes, FAIRification (making data Findable, Accessible, Interoperable, and Reusable), and Synthetic Data Generation (generating high-quality synthetic datasets that retain the statistical properties of real data while ensuring patient confidentiality).
This isn’t just about making AI work; it’s about making it work right. With the BETTER project leading the way, we are not just unlocking the potential of AI in healthcare. We are redefining what is possible. The road ahead is exciting, and conceptual modeling is lighting the path forward.
But there’s a challenge: how do we ensure that data from different hospitals, recorded in different formats and languages, can work together?
Without a shared structure, even the most advanced AI models would struggle to make sense of fragmented and inconsistent data. That’s where conceptual modeling becomes the secret ingredient that transforms scattered information into a unified and powerful resource for medical research. By creating a common language, conceptual modeling allows different datasets to “speak” to each other, ensuring that AI can learn from them in a meaningful way.
At the heart of the BETTER project, conceptual modeling plays a crucial role in three key areas: ETL (Extract, Transform, Load) processes, FAIRification (making data Findable, Accessible, Interoperable, and Reusable), and Synthetic Data Generation (generating high-quality synthetic datasets that retain the statistical properties of real data while ensuring patient confidentiality).
This isn’t just about making AI work; it’s about making it work right. With the BETTER project leading the way, we are not just unlocking the potential of AI in healthcare. We are redefining what is possible. The road ahead is exciting, and conceptual modeling is lighting the path forward.
