SPARK Seminar Series: Medical Imaging AI: Where do we go from here?

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. https://www.nature.com/articles/s41591-021-01614-0

This session is a part of the monthly seminar series organized by SPARK: AI Health Initiative for Medical Imaging. The seminar will highlight outstanding work in medical imaging at Duke and beyond. The seminar recordings will be publicly available.

The SPARK initiative focuses on development, validation, and clinical implementation of artificial intelligence algorithms for broadly understood medical imaging by bringing together the technical and clinical expertise across Duke campus. For more information and if you would like to get engage please contact Dr. Maciej Mazurowski (maciej.mazurowski@duke.edu).