Thursday July 30, 4:00 PM (EDT) Hosted by Duke Science & Society and Duke +Data Science Medical image
Thursday July 30, 4:00 PM (EDT)
Hosted by Duke Science & Society and Duke +Data Science
Medical image analysis with machine learning holds immense promise for accelerating the radiology workflow and benefiting patient care for COVID-19. Given the level of training and work involved in manually inspecting chest CT scans, there is significant interest in developing machine learning models that can automatically interpret chest CT images, including for COVID-19 patients. However, the use of machine learning and artificial intelligence (AI) in radiological imaging is relatively new, and codes of ethics and practice for use of AI in imaging are just now being contemplated by the medical community. This means that, in the effort to use AI to process radiological images for COVID-19 patients, decisions about how to acquire and curate the data and train the algorithms must be made in real time.
Join Duke+ DataScience, Duke Science & Society, and our panel of technical and ethical experts to discuss the ethics of using machine learning and AI to process radiological images for COVID-19 patients.
Dr. Timothy Dunn, Ph.D., Neurobiology, Harvard University; B.A. Molecular And Cell Biology, University of California at Berkeley; Postdoctoral Associate Department of Statistical Science; Duke Forge Scholar
Dr. Raymond Geis, Senior Scientist, American College of Radiology Data Service Institute; Adjunct Associate Professor of Radiology, National Jewish Heath, Denver, CO
Rachel Draelos, M.D., Ph.D Candidate Duke Medical Scientist Program, Duke University School Of Medicine; Ph.D Candidate, Duke University Department of Computer Science
Dr. Nita Farahany, J.D., PhD, Duke University; Director, Duke Initiative For Science & Society; Professor of Law and Philosophy
This Coronavirus Conversation accompanies two technical webinars on COVID+DS: Analysis of chest CT imaging data and connection to COVID diagnosis, and PyTorch for image analysis with deep learning.
This Coronavirus Conversation is eligible for RCR (Responsible Conduct of Research) credit. Attendees must indicate when registering that they are attending to receive RCR credit. During the event attendees will receive further instruction on how to obtain RCR credit for their attendance.
Coronavirus Conversations: A new, virtual event series from Duke Science & Society
As we practice social distancing, engage in online learning, and work remotely we are burdened with questions about how this pandemic is affecting our lives, the lives of those we love, and the society we are a part of. Over the course of this event series faculty and staff from Duke Science and Society will join academics, lawmakers, students, researchers, doctors, and others to shed some light on the events happening around us and what life will start to look like moving forward.
We will have moderated, casual lunch-time discussions with brief Q&A held via Zoom chat.
(Thursday) 4:00 pm
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