Explainable AI in continuously learning systems for heart failure

EXPLAIN-HF aims to develop explainable AI (XAI) models that improve the diagnosis and prognosis of heart failure, with a particular focus on right-ventricular dysfunction. Leveraging large multimodal cohorts such as the UK Biobank and extensive clinical imaging datasets from DHZC, the project seeks to identify mechanisms, risks, and predictors of disease progression. The models will be adapted for routine clinical use and integrated into a continuously learning system that updates and improves with real-world patient data. The project is ultimately delivering interpretable, robust AI tools to support clinical decision-making in cardiovascular medicine.