Zhi Huang, PhD

Abstract

Healthcare is approaching one-fifth of the U.S. economy, yet with inequitable access, first-in-human-history demographic shift to older age, and unsustainably inflated per capita costs; this gathering storm underscores the urgent need for innovative solutions. Recently, breakthroughs in foundation models and generative AI present exciting opportunities for transforming the healthcare landscape. In this talk, I will discuss the critical role of building an integrated ecosystem for medical imaging and demonstrate how AI can assist pathologists to enhance diagnostic accuracy and efficiency across two pathology tasks. Finally, I will introduce our lab’s latest innovation “TissueLab”—a general agent AI system designed for comprehensive medical image analysis.

Biography

Zhi Huang, PhD, is an Assistant Professor of Pathology and Laboratory Medicine, with a secondary appointment in the Department of Biostatistics, Epidemiology, and Informatics at the University of Pennsylvania Perelman School of Medicine. After receiving a PhD in Electrical and Computer Engineering from Purdue University in August 2021, he completed a postdoctoral training at Stanford University from 2021 to 2024. Dr. Huang’s research focuses on advancing AI and machine learning in medicine, including the development of vision-language foundation models for pathology (featured on the cover of Nature Medicine 2023), pathologists-AI collaboration (Nature Biomedical Engineering 2024), neurodegenerative disease research (Nature Communications 2023), as well as work on optimizing large language models (Nature 2025). His work has been covered by The New York Times, Stanford Magazine, and Stanford Scope. He also serves on the program committee for the RECOMB 2025 conference.