2024
12:00 PM - 01:00 PM
Tuesday Tue
Location
Stanford University School of Medicine
291 Campus DrStanford, CA 94305
Medical Physics Seminar - Issam El Naqa
Translation of AI into oncology clinical practice
Time:
12:00pm – 1:00pm Seminar & Discussion
Location:
Zoom Webinar
Webinar Registration:
https://stanford.zoom.us/webinar/register/WN_Y5QwpIWMQ3ugvdnzT_iyEQ
Check your email for the Zoom webinar link after you have registered
Speaker
Dr. Issam El Naqa, Ph.D., Founding Chair of the Department of Machine Learning at Moffitt Cancer Center & Professor of Oncological Sciences
Issam El Naqa, PhD is the founding chair of the department of Machine Learning at Moffitt Cancer Center and Professor in oncological sciences Tampa, Florida. He is a certified Medical Physicist by the American Board of Radiology. He is a recognized authority in the fields of machine learning, data analytics, and oncology outcomes modeling and has published extensively in these areas with more than 250+ peer-reviewed journal publications, and 5 edited textbooks. He has been a member and fellow of several academic and professional societies including AAPM, IEEE, AIMBE. His research has been funded by several federal and private grants in Canada and the USA and served on national and international study sections. He acts as a peer-reviewer and editorial board member for several leading international journals in his areas of expertise.
Abstract
Artificial intelligence (AI) is a transformative technology that is capturing popular imagination and can revolutionize biomedicine. AI and machine learning (ML) algorithms have the potential to break through existing barriers in oncology research and practice such as automating workflow processes, personalizing care, and reducing healthcare disparities. Despite this excitement, only few AI/ML models have been properly validated and fewer have become regulated products for routine clinical use. In this review, we highlight the main challenges impeding AI/ML clinical translation. We will present different clinical use cases from the domains of radiology, radiation oncology, immunotherapy, and drug discovery in oncology. We will attempt to summarize the lessons learnt from these cases.
A video of the presentation will be available after the webinar.