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 Chair:
William Oh, Yale
PMWC Award Ceremony
• Nigam Shah, Stanford
• Thomas Fuchs, Lilly
Keynote
• Thomas Fuchs, Lilly
Keynote
• Nigam Shah, Stanford
Real-World Evidence & Clinical AI: Closing the Loop Between Data and Care
• Chair: Roxana Daneshjou, Stanford
• Aashima Gupta, Google
• Brigham Hyde, Atropos Health
• Michael Pfeffer, Stanford
• Thomas Fuchs, Lilly
From Data to Decisions: Building Regulatory-Grade RWE from EHR Systems in Oncology
• Chair: Kate Estep, Flatiron Health
AI and Real-World Evidence: Building a Learning Health System for Precision Care
• Rich Gliklich, OM1
AI for Clinical Decision Support: From Models to Bedside
• Chair: Amrita Basu, UCSF
• Anurang Revri, Stanford
• Emily Alsentzer, Stanford
• Okan Ekinci, Roche
AI for CDS: From Models to Bedside
• Zachary Ziegler, OpenEvidence
Operationalizing AI in Health Systems: Trust, Adoption & Outcomes
• Chair: Danton Samuel Char, Stanford
• Matthew Solomon, Sutter Health
• Karan Singhal, OpenAI
• Sina Bari, iMerit Technology
• Shashi Shankar, Novellia
• Hal Paz, Khosla Ventures
Safe, Scalable AI in Clinical Practice: What’s Working and What’s Not
• Chair: Vincent Liu, Kaiser
• David Entwistle, Stanford Health Care
AI for Precision Psychiatry: Integrating Multimodal Data Into Clinical Decision Support
• Erwin Estigarribia, HEADLAMP health
Workflow-First Clinical AI: Integration Patterns, Guardrails & Change Management
• Jorge Duran, Klick Health
From Patient-Generated Data to Regulatory-Grade RWE: Design, Bias & Outcome Linkage
• Greg Bowyer, Evidation
