Speaker Profile
Ph.D., Assistant Professor, Stanford
Biography
Dr. Emily Alsentzer develops machine learning and natural language processing methods to support clinical decision-making and expand access to high-quality healthcare. Her research focuses on building clinically deployable models that are generalizable, robust, and integrated with medical expertise to ensure safe and responsible use in healthcare workflows. Previously, she was a postdoctoral fellow at Brigham and Womens Hospital, where she helped implement ML models across the Mass General Brigham health system. She received her PhD in the HarvardMIT Health Sciences and Technology program and BS and MS degrees from Stanford. Professor Alsentzers work bridges computer science and clinical communities, with publications in venues such as NeurIPS, NAACL, NEJM AI, Lancet Digital Health, Nature Communications, NPJ Digital Medicine, CHIL, ML4H, PSB, JAMIA, and Communications Medicine. She has also served as General Chair for ML4H and CHIL and as a founding organizer for SAIL and CHIL.
Session Abstract – PMWC 2026 Silicon Valley
Track Co-Chairs:
- William Oh, Yale Cancer Center
- David Reese, Amgen
Patient-centric data, such as Real-World Evidence (RWE) and Real-World Data (RWD), has become critical in reshaping drug development and healthcare decision-making. Over the last few years, regulatory agencies like the FDA and EMA have increasingly embraced RWE/RWD for decision-making processes, influencing everything from new drug indications to post-marketing surveillance. The integration of RWE and RWD is not only supporting clinical trial design and regulatory approvals, but also enabling precision medicine by providing deeper insights into patient subpopulations and their outcomes
Sessions:
- TBA
