Abstract: 
The presentation will outline recent advances in disease prediction through the integration of national health registries, genomic data, and artificial intelligence. Using data from the Finnish FinRegistry (over 7 million individuals and 6.5 billion records), large-scale machine learning models achieve high predictive accuracy for disease outcomes but also reveal disparities across regions and socioeconomic groups, emphasizing fairness and generalizability challenges. The presentation will further demonstrate how polygenic scores (PGS) capture lifelong disease risk and complement electronic health record–derived phenotype risk scores (PheRS), with each excelling for different disease categories. Combining genomic and EHR data enhances trial emulation, strengthens causal inference, and supports the design of more representative clinical studies. The talk will underscore that equitable, ethically guided AI and genetic integration are key to realizing precision prevention at a population scale.

To schedule a meeting with Dr.Ganna on Friday (early afternoon), please reach out to Arianna Rinaldi at arianna.rinaldi@uniroma1.it.