| Foundation Models for Biomedical AI |
| Shekoofeh Azizi |
| November 17, 2023, Friday, 2:00 PM - 3:00 PM EST |
| Abstract The emergence of foundation AI models offer a significant opportunity to rethink development of medical AI, making it more accessible, safer and equitable. A foundation model is a large artificial intelligence model trained on a vast quantity of data at scale, often by self-supervised learning. This process results in a model that can be adapted to a wide range of downstream tasks with need for little labeled data. These models are thus generalist models that can rapidly adapt and maintain performance in new tasks and environments. In this talk we explore the potential of foundation models in medicine and highlight some major progress towards creating generalist medical foundation models. |
| Bio Dr. Azizi is a staff research scientist and a research lead at Google DeepMind. Her research is focused on developing approaches that facilitate the translation of AI solutions into tangible clinical impact. She is particularly interested in designing foundation models for biomedical applications and I have been leading multiple major efforts in this area, including the moonshot project behind Med-PaLM and Med-PaLM 2, Google's flagship medical large language models. She is also lead researcher of Med-PaLM M the first demonstration of a generalist biomedical AI. Her research has been published in well-regarded journals and conferences such as Nature, Nature Medicine, Nature Biomedical Engineering, and CVPR and it serves as the cornerstone for multiple medical device products undergoing clinical trials at Google. Her research has been covered in various media outlets and recognized by multiple awards including the Governor General's Canada Academic Gold Medal for contributions in improving diagnostic ultrasound. |