Advances in numerical techniques and the ever increasing computational power have rendered the execution of forward models feasible. Using such models based on clinical images and parameterized to reflect a given patient's physiology, are a highly promising approach to comprehensively and quantitatively characterize cardiovascular function in a given patient. Such models are anticipated to play a pivotal role in future precision medicine as a method to stratify diseases, optimize therapeutic procedures, predict outcomes and thus better inform clinical decision making. Key challenges to be addressed are two-fold. Expensive computational models must be made efficient enough to be compatible with clinical time frames and generic models must be specialized based on clinical data which requires complex parameterization and data assimilation procedures.