An industrial webinar is offered by the MICCAI society on November 4th, at 5:30pm. This webinar, entitled "Lessons on the path from Code to Clinic", will feature Dr Yun Liu from Google Health.
Abstract
Inspired by the potential of artificial intelligence (AI) to improve access to expert-level medical image interpretation, several organizations began developing deep learning-based AI systems for detecting diabetic retinopathy (DR) from retinal fundus images around 2015. Today, these AI-based tools are finally being deployed at scale in certain parts of the world, often bringing DR screening to a population lacking easy access to timely diagnosis. The path to translating AI research into a useful clinical tool has gone through several unforeseen challenges along the way. In this talk, we share some lessons contrasting a priori expectations (“myths”) with synthesized learnings of what truly transpired (“reality”), to help others who wish to develop and deploy similar medical AI tools.
Biography
Dr Yun Liu is a staff research scientist in Google Health. In this role he focuses on developing and validating machine learning for medical imaging across multiple fields: pathology, ophthalmology, radiology, and dermatology. Yun completed his PhD at Harvard-MIT Health Sciences and Technology, where he worked on predictive risk modeling using biomedical signals, medical text, and billing codes. He has previously also worked on predictive modeling for nucleic acid sequences and protein structures. Yun completed a BSc in Molecular and Cellular Biology and Computer Science at Johns Hopkins University.