Medical AI: Intelligent, Safe, and Useful?

Can AI safely automate medical decision-making tasks to improve patient
outcomes? In this talk, I share the challenges in the development and
translation of medical AI, and how we are addressing them through a blend
of innovation in algorithm development, dataset curation, and
implementation design. I will first talk about self-supervised learning
methods for medical image classification that leverage large unlabeled
datasets to reduce the number of manual annotations required for
expert-level performance. Then, I will talk about open benchmarks that can
help the community transparently measure advancements in generalizability
of algorithms to new geographies, patient populations, and clinical
settings. Third, I will share insights from studies that investigate how to
optimize human-AI collaboration in the context of clinical workflows and
deployment settings. Altogether, this talk will cover key ways in which we
can realize the potential of medical AI to make healthcare more accurate,
efficient and accessible for patients worldwide.