Personalized Inference from Randomized Clinical Trials to Real-World Care

Learn how machine learning and artificial intelligence can transform randomized clinical trials (RCTs) to enable precision medicine and improve clinical care delivery. Explore methodological advances that allow for individualized interpretation of clinical trial results, moving beyond population-level findings to personalized treatment recommendations. Discover techniques for quantifying how well RCT evidence applies to real-world patient populations and clinical settings. Examine innovative data-driven and technology-enabled approaches that can accelerate the generation of clinical evidence and optimize various aspects of trial design. Understand how emerging AI tools can maximize RCT efficiency while broadening their role in guiding precision care decisions. Gain insights into the intersection of statistical machine learning, computer vision, and digital biomarkers for cardiovascular disease phenotyping. Access expertise from leading researchers at Yale University's Cardiovascular Data Science Lab who demonstrate practical applications spanning electronic health records, electrocardiography, cardiovascular imaging, and wearable devices in modernizing healthcare delivery.