Workflow, Accuracy, & QA Improvement Powered by AI

Due to the complexity of delivering high quality cancer care, there has been a natural progression of new technologies and innovations that aim to help simplify workflow and support faster and better radiation therapy techniques. One area of ongoing exploration has been the use of artificial intelligence to increase the standardization of treatment planning, shortening the time to initiating therapy, and with the possibility of generating lower toxicity and improving outcomes.


Join the clinical team from the University of Pennsylvania, Department of Radiation Oncology, as they describe how they have used AI to support significant time savings in workflow, greater accuracy, and improve quality assurance. The team covers how AI-based, prognostic models have positively impacted patient care – highlighting dosimetry and optimization considerations and presenting current data on results and outcomes.

Alexander Lin, MD – Professor of Radiation Oncology
Ontida Apinorasethkul, MS, CMD – Photon Dosimetry Manager
Steven Philbrook, MMP – Medical Physicist
Rafe McBeth, PhD – Assistant Professor of Radiation Oncology