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Latest Events

EIPM AI Working Group: "Agentic and Foundational AI in Healthcare - Obstacles, Opportunities, and a Vision for near Future"
March 20, 2026
Mar 20, 2026
11:00 AM - 12:00 PM
11:00 AM

Englander Institute for Precision Medicine | AI Working Group

 

"Agentic and Foundational AI in Healthcare - Obstacles, Opportunities, and a Vision for near Future"

 

Presented by Narges Razavian, PhD

Associate Professor, Center for Healthcare Innovation and Delivery Science,

NYU Medical School 

Health IT’s Legacy and the Road to Responsible AI
March 26, 2026
Mar 26, 2026
6:00 PM - 7:15 PM
6:00 PM

Why does health IT still frustrate so many healthcare stakeholders? Why were early systems designed with limited clinician input? Can AI help fix the problems created by legacy systems?

 

You’re invited to join us for a conversation with Dr. S. Yin Ho, author of Rushing Headlong: Health IT’s Legacy and the Road to Responsible AI. 

 

In her new book, Dr. Ho examines how the early architecture of health information technology shaped the systems that clinicians and researchers struggle with today. As generative AI arrives, those same structural choices may determine whether AI ultimately better serves practitioners and patients or simply adds another layer of complexity.

 

Dr. Ho is a serial entrepreneur and visionary executive in health IT and clinical research with more than 25 years of experience founding, building, and leading healthcare technology companies and initiatives.  Most recently, she served as Interim CEO of Veradigm, where she led the strategic acquisition of generative AI company ScienceIO. Under her leadership, Veradigm became the first electronic health record company to develop proprietary small language models in-house—an effort aimed at improving the quality of clinical data used for both care and research.

 

Join us for a candid discussion about the past, present, and future of health IT.  Register here to attend in person or on Zoom.

Education Programs

Applies IT, data science, and human factors to improve healthcare; 12-month option plus capstone with industry partners.

 

Hands-on training in statistics, machine learning, and data science to analyze biomedical and health data; 16-month track.

Two-year dual degree focused on digital health, combining advanced CS with real-world health tech projects and a studio capstone.

 

Interdisciplinary PhD leveraging methods like single-molecule imaging and machine learning to probe biological systems.

Prepares researchers to study determinants of health and healthcare delivery using biostatistics, epidemiology, informatics, and policy.

Graduate training that integrates quantitative tools with biology to study mechanisms across scales.


 

Tri-institutional PhD across Cornell, Weill Cornell Medicine, and MSK, with broad research options and strong publication outcomes.

 

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