The field of diagnostic radiology is at a critical inflection point. Artificial intelligence powered systems are increasingly being developed to match or out-perform trained physicians in the field.
A recent great example of AI in diagnostic radiology is the CheXNet project from Stanford, that uses deep-convolutional neural nets to analyze one of the largest publicly available chest x-ray data-set (https://stanfordmlgroup.github.io/projects/chexnet/)
One of the key benefits of such automated image analysis techniques is to improve the existing workflow of classifying an image and applying it to reduce medical errors.
Dr. Matthew Spotnitz, M.D., M.P.H. is a physician with clinical experience in Radiology and Surgery. His presentation will discuss the kinds of errors that occur in diagnostic radiology, why they happen, and how AI can reduce them.
During this Healthcare AI talk by Matt, he will also give an overview on physician perspective of the current AI revolution in healthcare.
Limited seating. Entry to the event based on first come first served basis.
Please bring an ID to gain access to the building.
There is a cover fee of $20, by cash, at the entrance.