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Ever wondered how artificial intelligence could revolutionize the healthcare industry? Join us on MedEvidence as we sit down with Dr. Ittai Dayan, a distinguished physician and data scientist who shares his expertise in emergency medicine, neuroimmunology, and AI development at Mass General Brigham. You'll gain an understanding of federated learning, a groundbreaking method that allows data processing without transferring data, fostering collaboration among healthcare systems. We also differentiate between digital transformation, traditional health IT, and AI, addressing the enthusiasm and skepticism surrounding AI's role in medicine.

In this episode, we delve into AI hallucination in medical contexts, contrasting human and machine error while discussing the limitations of generative AI models in clinical setups. Dr. Dayan highlights the critical importance of product safety measures, ongoing validation, and vigilant monitoring. Discover the creation of a sophisticated multimodal algorithm by Rhino Health aimed at predicting patient outcomes in emergency departments, and learn about the hurdles of data sharing, regulatory challenges, and commercialization in the ever-evolving field of medical AI.

Talking Topics:

• Artificial Intelligence in Medicine Explained
• Federated Learning in Healthcare
• Advancing Healthcare Technology With Rhino Health

Learn more about Dr. Ittai Dayan:
As co-founder and CEO of Rhino Health, Dr. Ittai Dayan transforms how 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 - across increasingly diverse patient populations. He led the EXAM study, published in Nature Medicine, the world’s largest and most prominent study to-date utilizing federated learning (FL) to train a healthcare AI solution on diverse data across institutions.

Ittai graduated from the Johns Hopkins Bloomberg School of Public Health. He earned his MD and his Bachelors of Science from The Hebrew University of Jerusalem. He serves on the Editorial Board of Nature Digital Medicine, a leading publisher of peer-reviewed scientific studies. His own research has been published in journals including those from Nature group, IEEE’s Journal of Biomedical and Health Informatic

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Have a question for Dr. Koren? Email him at [email protected]

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