Visual-language model for digital pathology and clinical decision-making

Zhi Huang is a postdoc at Stanford University. In August 2021, he received his Ph.D. degree from Purdue University, majoring in Electrical and Computer Engineering (ECE). Prior to that, he received his Bachelor of Science degree in Electronic and Information Engineering (BS-MS direct entry class) from Xi’an Jiaotong University. His background is in the area of Artificial Intelligence, Human-AI Collaboration, Digital Pathology, and Precision Medicine. In 2022, Zhi and his advisors co-founded nuclei.io - the AI platform for digital pathology. It was selected as one of the nine Stanford Catalyst innovations.
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 seminar, I will share how to train a visual-language foundation model for pathology using publicly available medical images, and how that publicly shared medical information can be harnessed to enhance diagnosis, knowledge sharing and education. In addition, I will discuss the benefit of human-AI collaboration across two diagnosis tasks.