The Use of AI to Improve Access to High-Quality Radiation Therapy Treatment Planning in Low- and Middle-Income Countries
The Use of AI to Improve Access to High-Quality Radiation Therapy Treatment Planning in Low- and Middle-Income Countries
Abstract:
Advances in artificial intelligence (AI) are going to affect all aspects of radiation therapy, including contouring, treatment planning, and quality assurance. This presentation will describe our efforts to develop and expand these tools specifically for clinics in low- and middle-income countries. We will describe the expected quality (clinical acceptability, efficiency gains) of these tools, as well as possible risks in deployment and how to mitigate them. Finally, we will describe lessons learned in clinical deployment.
Learning Objectives:
- To be able to describe how AI is likely to improve access to high-quality radiotherapy planning across the world.
- To understand risks when implementing AI into clinical practice, and possible ways to reduce them.
- To understand challenges in implementing these tools into clinical practice in LMICs.
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Moderator:
M. Mahesh, MS, PhD. Chair of Science Committee – IOMP.
Speakers:
Laurence Court, PhD, University of Texas MD Anderson Cancer Center, USA.
Barbara Marquez, University of Texas MD Anderson Cancer Center, USA.
Christoph Trauernicht, PhD, Tygerberg Hospital / Stellenbosch University, South Africa.