MedAI #78: Foundation Models for Medical AI | Vivek Natarajan

Title: Foundation Models for Medical AI
Speaker: Vivek Natarajan
Abstract:
Despite recent progress in the field of medical artificial intelligence (AI), most existing models are narrow, single-task systems that require large quantities of labeled data to train. Moreover, these models cannot be easily reused in new clinical contexts and lack expressivity and interactive capabilities. This, in turn, has prevented their broad uptake in real-world healthcare settings. The advent of foundation models offers an opportunity to rethink medical AI and make it more performant, interactive, safer, and equitable. In this talk, I will share some of my recent work in the space of foundation medical models: REMEDIS, Co-Doc and Med-PaLM. In particular, I will highlight the effectiveness of these models in solving key medical AI translation problems such as data-efficient generalization, reliability and safety. Finally, I will also lay out how we might be able to build on these models towards generalist multimodal, multi-task medical AI.
Speaker Bio:
Vivek Natarajan is a Staff Research Scientist at Google Health AI advancing biomedical AI to help scale world class healthcare to everyone. Vivek is particularly interested in building large language models and multimodal foundation models for biomedical applications and leads the Google Brain moonshot behind Med-PaLM, Google's flagship medical large language model. Vivek's research has been published in well-regarded conferences and journals including Nature Medicine, Nature Biomedical Engineering, NeurIPS, CVPR, ICCV and JMLR. Prior to Google, Vivek worked on multimodal assistant systems at Facebook AI Research and published award-winning research, was granted multiple patents and deployed AI models to products at scale with hundreds of millions of users.
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If I am not mistaken, this covers Foundation Models for images and language. Any attempt to build foundation models for time-series health data? Thanks in advance