Raúl research focuses on developing quantitative imaging biomarkers for chronic lung diseases, including Chronic Obstructive Pulmonary Disease (COPD), Pulmonary Vascular Disease (PVD) and Interstitial Lung Disease (ILD). Dr. San José Estépar is a pioneer in using artificial intelligence and deep learning techniques to analyze chest CT scans, contributing significantly to the understanding of pulmonary vascular remodeling and parenchymal lung injury. He leads the development of the open-source Chest Imaging Platform, empowering systems biology discovery and novel surrogate targets for drug discovery and clinical trial development. As a Principal Investigator for numerous NIH awards and industry-sponsored studies, Raúl has been instrumental in major multicenter national studies, including COPDGene, the Framingham Heart Study Pulmonary Research Center, the CARDIA Lung Study, the ALA Lung Health Cohort, and the RECOVER study for long COVID. His innovative research in imaging biomarkers has resulted in over 250 peer-reviewed publications and several patents.

Prior areas of interest included the development of techniques for the processing of Diffusion Tensor Imaging (DTI). He also designed and implementated image-guided approaches for laparoscopic (IRLUS) and endoscopic (IRGUS) interventions guided by ultrasound and its application to Natural Transluminal Endoscopic Surgery (NOTES).  His image analysis interest includes the low level operators based on tensor image analysis, image segmentation and registration and the application of deep learning approaches to medical imaging applications. Raúl is also an affiliated member of the Laboratory of Mathematics in imaging (LMI) and the Surgical Planning Laboratory (SPL).

Raúl received his M.Sc. and Ph.D. in Telecommunications Engineering from the University of Valladolid, Spain, and conducted his post-doctoral training at Harvard Medical School. He is a member of the IMPACT program at MIT and the Fleischner Society.