AIMI Grand Rounds: Multimodal Foundation Models for Precision Oncology - Ruijiang Li, PhD

The AIMI Grand Rounds, sponsored by the Center for Artificial Intelligence in Medicine and Imaging (AIMI), is a virtual series held on every fourth Tuesday. The series is a crucial initiative for disseminating the latest AI advancements in medicine, aiming to drive transformative innovations in healthcare. Stanford participants of the live event can claim 1.0 CME credits: AMA PRA Category 1 Credits ™ or Non-Physician Participation Credit.

Speaker:

Ruijiang Li, PhD: Associate Professor of Radiation Oncology, Stanford University

Bio: Dr. Ruijiang Li is an Associate Professor of Radiation Oncology at Stanford University School of Medicine. He is also an affiliated faculty member of Stanford Institute of Human-Centered Artificial Intelligence (HAI) and Stanford Cancer Institute. Dr. Li’s research is in the broad field of artificial intelligence (AI) and precision medicine. Specifically, his work focuses on developing AI tools for biomedical data analysis and personalized cancer treatment. 

Abstract: Clinical decision-making demands the integration of diverse data — medical images, clinical narratives, and omics profiles. AI methods that effectively leverage multi-modal data hold transformative potential for patient care. A particularly promising domain is digital pathology, where AI has demonstrated strong capacity to enhance cancer diagnosis and inform treatment strategies. This talk explores how multi-modal foundation models can extract clinically meaningful insights from digital pathology, with a focus on prognostic and predictive biomarkers for precision oncology. I will present our recent work developing vision-language foundation models and approaches that integrate histopathology with spatial proteomics, illustrating how these advances can improve diagnostic accuracy and guide precision cancer treatment.