Dr. Dayan is transforming the way healthcare AI solutions are created, adopted and measured. The Rhino Health Platform provides access to a large, distributed dataset from a diverse group of patients, powering models that deliver consistent results and, ultimately, improve health outcomes for large populations of patients. Drawing on his background as a clinician and researcher, Ittai is passionate about creating equitable access to advanced AI-based diagnostics and treatment pathways. He led the EXAM study, published in Nature Medicine, which is the world’s largest study to-date utilizing federated learning (FL) to train a healthcare AI solution on diverse data across institutions. Previously, Ittai served as an Executive Director at Mass General Brigham, where he led the Center for Clinical Data Science (CCDS). Ittai has studied at the Johns Hopkins Bloomberg School of Public Health. He earned his MD and his Bachelors of Science from The Hebrew University of Jerusalem.
Accelerating Healthcare AI with Federated Learning
Researchers worldwide are utilizing a new approach to collaborative creation of AI models: federated learning (FL). No moving or replicating data. Always protect privacy. Ultimately, this will bring to market new AI-based solutions that improve outcomes for increasingly diverse patient populations. Learn about FL and the future of precision medicine.
Machine learning and other AI techniques have great potential for advancing discovery and realization of precision medicine. Because of the distributed and personalized nature of precision medicine, issues related to data aggregation, proper curation and privacy-preservation are amplified. This talk will address these unique problems and means of addressing them.