2026
12:00 PM - 01:00 PM
Tuesday Tue
Location
Medical Physics Seminar - Kun-Hsing Yu
Trustworthy AI for Pathology: Quantifying Uncertainty and Ensuring Fairness
Time:
12:00pm – 1:00pm Seminar & Discussion
Location:
Zoom Webinar
Webinar Registration:
https://stanford.zoom.us/webinar/register/WN_81Ma0nwgTg-vbboO8U452g
Check your email for the Zoom webinar link after you have registered
Speaker
Dr Kun-Hsing “Kun” Yu, M.D., Ph.D., Associate Professor in the Department of Biomedical Informatics at Harvard Medical School
Kun-Hsing “Kun” Yu, M.D., Ph.D., is an Associate Professor in the Department of Biomedical Informatics at Harvard Medical School. He pioneered the first fully automated artificial intelligence (AI) algorithm capable of extracting thousands of features from whole-slide pathology images. His research has uncovered molecular mechanisms driving the microscopic phenotypes of tumor cells and identified novel cellular morphologies that predict patient prognosis.
Dr. Yu’s lab integrates multi-omics (e.g., genomics, epigenomics, transcriptomics, and proteomics) profiles with quantitative pathology patterns to predict clinical phenotypes in cancer patients. The AI methods developed by the Yu Lab have been independently validated by over 80 research laboratories worldwide.
His contributions to AI in pathology have earned numerous honors, including the National Institutes of Health (NIH) Maximizing Investigators’ Research Award, Google Research Scholar Award, American Medical Informatics Association New Investigator Award, Harvard Medical School Dean’s Innovation Award, Department of Defense (DoD) Career Development Award, and the American Cancer Society (ACS) Research Scholar Award. He is a Fellow of the American Medical Informatics Association (FAMIA).
Abstract
Artificial intelligence (AI) is reshaping the landscape of cancer research and clinical diagnosis. Recent advances in microscopic image digitization, multi-modal machine learning algorithms, and scalable computing have enabled AI-powered pathology at an unprecedented scale. In this talk, I will highlight recent breakthroughs in uncertainty-aware pathology foundation models and their effectiveness in analyzing high-resolution digital pathology images. In addition, I will present new approaches in fairness-aware pathology AI that mitigate performance differences across populations and institutions. Furthermore, I will discuss recent studies that leverage AI to uncover novel connections between cell morphology and molecular profiles. Finally, I will outline persistent challenges and future directions in developing robust, generalizable, and clinically deployable medical AI systems.