Projects

Our primary objective is to advance the generalizability and inclusiveness of clinical trials through the integration of artificial intelligence and in-silico modeling.
To achieve this, we will develop advanced data processing and machine learning tools capable of identifying subject categories within trials, normalizing heterogeneous datasets from multiple clinical institutions, and managing missing data through modern statistical and data science methods. The project will further employ generative models to include under-represented populations, creating in-silico cohorts that simulate how diverse groups might respond to interventions.
Our project particularly focuses on improving trust, diversity, and representativeness in evidence-based medicine by enabling the automatic assessment of a trial’s generalizability to specific patients and by estimating how results translate to real-world scenarios. The project’s outcomes will directly inform future guidelines, supporting the development of robust, equitable, and generalizable clinical research practices.