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The dream of precision health is to develop a data-driven, continuous learning system where new health information is instantly incorporated to optimize care delivery and accelerate biomedical discovery. In reality, the health ecosystem is plagued by overwhelming unstructured data and unscalable manual processing. Self-supervised AI such as large language models (LLMs) can supercharge structuring of biomedical data and accelerate transformation towards precision health.

In this talk, Hoifung Poon, PhD will present research progress on generative AI for precision health, spanning biomedical LLMs, multi-modal learning, and causal discovery. This enables his team to extract knowledge from tens of millions of publications, structure multimodal real-world data for millions of cancer patients, and apply the extracted knowledge and real-world evidence to advancing precision oncology in deep partnerships with real-world stakeholders.


Hoifung Poon, Phd
General Manager at Health Futures in Microsoft Research and an affiliated faculty
University of Washington Medical School