Exploring Foundation Models for Generalist Medical AI
Monday, 11.09.2023, 16:45 – 17:45
Barkausen-Bau BAR/SCHÖ/E
by Shekoofeh Azizi
Shekoofeh Azizi is a senior research scientist at Google Deepmind and she completed her Ph.D. at the University of British Columbia (UBC), Vancouver, Canada in 2018. Her research concentrated on developing simple and efficient machine learning algorithms that are broadly applicable to a range of computer vision applications. Specifically, over the past few years, she has been focused on developing methods to accelerate the translation of AI solutions to clinical impact. Her work has been covered in various media outlets and recognized by multiple awards including the Governor General’s Canada Academic Gold Medal for her contribution in improving diagnostic ultrasound.
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.