Learning objectives

1. Identify and address key challenges in medical foundation models.
2. Implement strategies to improve fairness, robustness, and clinical relevance in AI healthcare applications.
3. Critically evaluate the trade-offs between general-purpose and specialized medical foundation models, especially in light of cost and performance.
4. Develop practical approaches to ensure AI in healthcare is trustworthy, impactful, and aligned with real-world clinical needs.

Speaker biography

Dr. Xiaoxiao Li is currently an assistant professor in the Department of Electrical and Computer Engineering at the University of British Columbia, a faculty member at Vector Institute. Dr. Li is recognized as a Canada Research Chair (Tier II) in responsible AI and a Canada CIFAR AI Chair. Dr. Li’s research interests primarily lie at the intersection of AI and healthcare, theory and techniques for artificial general intelligence (AGI), and AI trustworthiness. Dr. Li aims to develop the next-generation responsible AI algorithms and systems.