Uploaded January 2026 | Updated September 2026, 2 weeks ago
In this video, Evangelos Oikonomou and Rohan Khera (Yale) presents 'Translating Personalized Inference from Randomized Clinical Trials to Real-World Clinical Care'.
Presentation Date: 06/05/24
Abstract: Randomized clinical trials (RCT) represent the cornerstone of medical evidence generation. Emerging advances in machine learning and artificial intelligence can broaden the role of RCTs in guiding precision care. Moreover, these tools can also maximize the efficiency of RCTs through applications that optimize various facets of their design. The session covers a range of innovations in RCTs, including methodological advances to guide the individualized interpretation of clinical trials, the quantification of the real-world applicability of RCT evidence, and novel data-driven and technology-enabled methods to accelerate clinical evidence generation.
Bio: Rohan Khera, M.D., M.S.: Dr. Rohan Khera is a Cardiologist-Data Scientist at Yale University, where he leads the Cardiovascular Data Science (CarDS) Lab. He is also the Clinical Director of Health Informatics at CORE and serves as an Associate Editor at JAMA. His research focuses on using AI and data science for precision patient care in cardiovascular medicine and is supported by grants from NIH and Doris Duke Charitable Foundation. The work spans broad digital data sources, including the electronic health record, electrocardiography, cardiovascular imaging, and wearable devices, with applications that seek to modernize US and global healthcare. Dr. Khera is a recipient of numerous awards, including the ASCI Young Physician-Scientist Award and the Blavatnik Award.
Evangelos Oikonomou, M.D. D.Phil.: Dr. Evangelos Oikonomou is a cardiovascular medicine fellow at Yale University and a post-doctoral fellow in the Yale Cardiovascular Data Science (CarDS) Lab. His work focuses on the intersection of statistical machine learning, computer vision, and digital biomarkers for the precise and scalable phenotyping of cardiovascular disease. He is the recipient of an F32 fellowship from the NHLBI and young investigator awards sponsored by the European Society of Cardiology, American Heart Association and Northwestern Cardiovascular Young Investigator Forum, as well as the ASCI e-Gen award.
For more information, visit: broadinstitute.org
Copyright Broad Institute, 2025. All rights reserved.
In this video, Evangelos Oikonomou and Rohan Khera (Yale) presents 'Translating Personalized Inference from Randomized Clinical Trials to Real-World Clinical Care'.
Presentation Date: 06/05/24
Abstract: Randomized clinical trials (RCT) represent the cornerstone of medical evidence generation. Emerging advances in machine learning and artificial intelligence can broaden the role of RCTs in guiding precision care. Moreover, these tools can also maximize the efficiency of RCTs through applications that optimize various facets of their design. The session covers a range of innovations in RCTs, including methodological advances to guide the individualized interpretation of clinical trials, the quantification of the real-world applicability of RCT evidence, and novel data-driven and technology-enabled methods to accelerate clinical evidence generation.
Bio: Rohan Khera, M.D., M.S.: Dr. Rohan Khera is a Cardiologist-Data Scientist at Yale University, where he leads the Cardiovascular Data Science (CarDS) Lab. He is also the Clinical Director of Health Informatics at CORE and serves as an Associate Editor at JAMA. His research focuses on using AI and data science for precision patient care in cardiovascular medicine and is supported by grants from NIH and Doris Duke Charitable Foundation. The work spans broad digital data sources, including the electronic health record, electrocardiography, cardiovascular imaging, and wearable devices, with applications that seek to modernize US and global healthcare. Dr. Khera is a recipient of numerous awards, including the ASCI Young Physician-Scientist Award and the Blavatnik Award.
Evangelos Oikonomou, M.D. D.Phil.: Dr. Evangelos Oikonomou is a cardiovascular medicine fellow at Yale University and a post-doctoral fellow in the Yale Cardiovascular Data Science (CarDS) Lab. His work focuses on the intersection of statistical machine learning, computer vision, and digital biomarkers for the precise and scalable phenotyping of cardiovascular disease. He is the recipient of an F32 fellowship from the NHLBI and young investigator awards sponsored by the European Society of Cardiology, American Heart Association and Northwestern Cardiovascular Young Investigator Forum, as well as the ASCI e-Gen award.
For more information, visit: broadinstitute.org
Copyright Broad Institute, 2025. All rights reserved.
