Aortic stenosis (AS) affects about 1.5 million people in the United States and is associated with a 5-year survival rate of 20% if untreated. In these patients, aortic valve replacement is performed to restore adequate hemodynamics and alleviate symptoms. The development of next-generation prosthetic aortic valves seeks to provide enhanced hemodynamic performance, durability, and long-term safety, emphasizing the need for high-fidelity testing platforms for these devices. We propose a soft robotic model that recapitulates patient-specific hemodynamics of AS and secondary ventricular remodeling which we validated against clinical data. The model leverages 3D-printed replicas of each patient's cardiac anatomy and patient-specific soft robotic sleeves to recreate the patients' hemodynamics. An aortic sleeve allows mimicry of AS lesions due to degenerative or congenital disease, whereas a left ventricular sleeve recapitulates loss of ventricular compliance and diastolic dysfunction (DD) associated with AS. Through a combination of echocardiographic and catheterization techniques, this system is shown to recreate clinical metrics of AS with greater controllability compared with methods based on image-guided aortic root reconstruction and parameters of cardiac function that rigid systems fail to mimic physiologically. Last, we leverage this model to evaluate the hemodynamic benefit of transcatheter aortic valves in a subset of patients with diverse anatomies, etiologies, and disease states. Through the development of a high-fidelity model of AS and DD, this work demonstrates the use of soft robotics to recreate cardiovascular disease, with potential applications in device development, procedural planning, and outcome prediction in industrial and clinical settings.