Research

My academic journey began with personalised modeling of vascular disease using medical imaging. Driven by the rapid advance of deep learning and the emergent potential of artificial general intelligence (AGI), my research evolved to focus on AI for Multi-Modal Medicine (AIM³).

Here, “multi-modal” encompasses more than just integrating various medical imaging modalities (e.g., CT, MR, PET). It also includes incorporating diverse medical data modalities (e.g., tabular data, waveforms, images and text) and assimilating relevant domain-specific scientific knowledge (e.g., mechanics, physics, and life sciences). Leveraging recent breakthroughs in AI and our growing understanding of biological systems, we aim to develop multi-modal foundation model and agents for the diagnosis and treatment of specific diseases.

Topics

My recent research focuses on developing AI models that effectively integrate and interpret information from multiple sources. These models are instantiated as foundation model-driven agents built on real-world healthcare big data. Key projects include:

• CardioGPT: A Multi-Modal Foundation Model for Cardiac Modeling

• RenalGPT: Precise Diagnosis and Treatment of Renal Cancer

• EndoGPT: Next-Generation Endoscopic AI Agent