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Radiation Therapy in the Era of Artificial Intelligence

D Nguyen1*, H Li2*, S Jiang3*, J Van Soest4*, L Conroy5*, G Valdes6*, (1) UT Southwestern Medical Center, Dallas, TX, (2) Carle Cancer Center, Urbana, IL, (3) UT Southwestern Medical Center, Dallas, TX, (4) Maastricht University Medical Centre, Maastricht, NL, (5) The Princess Margaret Cancer Centre, University Health Network, Toronto, ON, CA, (6) University of California San Francisco, San Francisco, CA


(Wednesday, 7/15/2020) 3:30 PM - 5:30 PM [Eastern Time (GMT-4)]

Room: Track 3

Over the past several years, artificial intelligence (AI) has impacted many aspects of human life, from home goods to automated driving, revolutionizing the way we interact and live in the world. These AI technologies are now being researched, developed, and applied to healthcare, including radiation therapy. Radiation therapy is one of the leading treatment methods for cancer patients, accounting for over half of cancer treatment, either standalone or in conjunction with another modality such as surgery or chemotherapy. There is considerable research demonstrating that AI frameworks can surpass their more classical counterparts in areas such as classification, segmentation, image processing, treatment planning, outcome prediction, and quality assurance.

In this session we will discuss the development and application of modern AI technologies in major areas of radiation therapy, including medical imaging, treatment planning, outcome and toxicity prediction, and error detection and prevention in the clinic. In addition, we will discuss a roadmap for robust AI in radiation therapy. While the integration of these AI technologies into our field of radiation therapy seems certain, opening a pathway to discussion and collaboration is necessary so we can carefully shape how AI will transform the field, and ultimately improve patient outcomes and quality of life.

Learning Objectives:
1. Understand the pathway and development of modern AI frameworks available and used today
2. Identify useful aspects of AI technologies and algorithms for problems in radiation therapy
3. Learn about the applications of AI in the several categories of radiation therapy
4. Learn about methods to increase the robustness and safety of AI technologies
5. Understand the current limits of AI within each category radiation therapy



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