Stanford Computational Medicine Research Colloquium | From Copilots to Clinical Intelligence
Zoom livestream: https://stanford.zoom.us/j/97887596012?pwd=bGVpbG1uL2ZBdEd2cHQzcVJMR3p6UT09
Webinar ID: 978 8759 6012 | Passcode: 420642
KEY INFO
When: Thursday, October 1, 2026, 12:00-1:00 p.m. Pacific
Where: Tapao Hall, 3180 Porter Drive, rooms B107 and B136, Palo Alto, CA 94304, or online via Zoom
SPEAKERS
Eric Karl Oermann is an Associate Professor of Neurosurgery, Radiology, and Data Science at NYU and the Director of the Health AI Research Lab of the NYU Langone Health System. He studied mathematics at Georgetown University with a focus on group theory. Dr. Oermann spent six months with the President's Council on Bioethics studying human dignity under the mentorship of renowned physician-philosopher Edmund Pellegrino prior to leaving graduate studies in mathematics at UNC to pursue medicine.
Dr. Oermann has won numerous awards for his scholarship including fellowships from the American Brain Tumor Association and Doris Duke Charitable Research Foundation. Dr. Oermann was selected as one of Forbes Magazine's 30 Under 30 for his work on using machine learning to develop prognostic models for cancer patients. He was recruited as an early researcher at Verily (Google Life Sciences), and spent time at Google-X on Project Amber (the Moonshot for Mental Health).
He has published over one-hundred manuscripts spanning basic research on machine learning, neurosurgery, and the philosophy of medicine, and has founded or co-founded multiple startups (Cadyne, Artisight, Delvi, MarchAI) in the medical AI space and consumer electronics. He is a practicing neurosurgeon and computer scientist, and is dedicated to studying human and artificial intelligence to improve human health and AI technologies themselves using insights from human neuroscience.
Krithik Vishwanath is a Churchill Scholar at the University of Cambridge, where he studies artificial intelligence in medicine, and a researcher in Dr. Eric Oermann's lab at NYU Langone Health. His work focuses on building and testing AI tools for clinicians, including recent studies evaluating general-purpose large language models against specialized clinical AI on medical benchmarks.
Before Cambridge, he worked on mathematical models of tumor growth and treatment response at UT Austin's Center for Computational Oncology, and on photoacoustic imaging at MD Anderson Cancer Center.
He holds degrees in computational engineering, mathematics, and chemistry from UT Austin.
THE TALK
This talk will begin by discussing the current state of medical AI in the literature and in the field.
We'll then review the state of evaluating these tools, emphasizing the different traditions of evidence between the machine learning community (benchmarking) and the medical community (clinical studies).
Finally, we'll discuss the strengths and weaknesses of both approaches with regards to medical AI, and attempt to describe ways towards synthesizing these opposing world views.
HOST & MODERATOR
Hosted by the Stanford Division of Computational Medicine. Moderated by Jonathan H. Chen, MD, PhD, PI at Stanford ARISE.
AFTER THE TALK
Recordings are posted on the ARISE podcast page within a week: https://www.arise-ai.org/podcast
KEEP IN TOUCH
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