WEHI Director’s Special Seminar hosted by Professor Tony Papenfuss

Zongyuan Ge

Associate Professor, Department of Data Science & AI – Monash University; Founding Director, Aim for Health Lab

Modern Medical AI with Large-Scale Models

 

Davis Auditorium

Join via SLIDO enter code #WEHIseminar

Including Q&A session
 

Associate Professor Zongyuan Ge has wide expertise in AI, Biomedical Engineering, Digital Health, and Medical AI Applications. He is the Founding Director of the Monash Medical AI group and the AI Director of the Monash Augmented Intelligence and Multimodal analytics for Health (AIM) lab, leading over 40+ talented PhD students, 6 research fellows and 6 tenured professors and lecturers. He has worked with clinical institutions such as Monash Health, the Alfred Health and Stanford Health Care the program has attracted grants of over $40M for his work. He is currently the Chief Scientist for the Monash-Airdoc Research Centre, a $3M+ incubator and investment fund, is responsible for developing CFDA, NMPA, TGA, CLASS III approved healthcare monitoring products. He holds 250+ academic publications in top journals such as Nature Medicine, The Lancet Digital Health, The British Medical Journal, JAMA Neurology, Nature Nanotechnology, Nature Communication and 40 patents. His work has been recognised by many international and national awards, including the Viertel Senior Medical Fellowship, the 200 Most Qualified Young Researchers in Computer and Mathematics by the Scientific Committee of the Heidelberg Laureate Foundation, IBM Scientific Research Accomplishment Award, NVIDIA AI Fellowship, Agilent Thought Leader Program, the Australian Pattern Recognition Society (APRS) Early Career Researcher Award, “the highest award of artificial intelligence in China”–Wu Wenjun Award for Artificial Intelligence Science and Technology twice in 2019, 2022, and Monash Exceptional Achievement Award. His standing as a top 2% highly cited researcher since 2021-2025 and recognition of his articles in Clarivate Analytics’ list of highly cited work reaffirm the impact of his research with citations 17000+ and 55 h-index.

This talk will define and motivate the problem of Medical AI Research, the challenges as well as recent progress at the Monash AIM for Health Lab. This includes component technologies such as deep learning, evaluation methodologies, human-ai interaction, and also end-to-end software-to-hardware systems for applications such as disease diagnosis, disease prediction and management, and treatment. The aim is to develop next-generation medical applications capabilities using the LLM and Foundation Model that reduce Australia’s increasingly burden of disease, and that enable transformation of Australia’s most important healthcare sectors through enhanced automation and medical AI technologies. Our research re-combines expertise in human stem cell biology, biomedical engineering, clinical research and artificial intelligence scientists. Our medical AI products have served millions of people in China, India, Japan, South Africa and Australia. This talk will highlight the transformative impact of foundation models in medical artificial intelligence, showcasing their application across diverse clinical domains. We will specifically explore our pioneering work in developing the Comprehensive AI Retinal Expert (CARE) system, a foundation model published in The Lancet Digital Health, which leverages over 230,000 color fundus photographs and is scaling with 100 million images for robust retinal analysis. Furthermore, we will introduce PanDerm, a novel foundation model for dermatology presented in Nature Medicine, which integrates four complementary imaging techniques to enhance early-stage melanoma detection and improve diagnostic accuracy for both specialists and non-specialists. These advancements, alongside our efforts in neurology AI for epilepsy management, underscore the potential of foundation models to revolutionize medical diagnosis, treatment, and ultimately, patient outcomes by effectively harnessing large-scale, multi-modality medical data.

All welcome!