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Program Information

Impact of Automatic Planning From Clinician's Perspective

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P Kupelian

P Kupelian1*, (1) David Geffen School of Medicine at UCLA, Los Angeles, CA

TH-E-BRCD-4 Thursday 1:00:00 PM - 2:50:00 PM Room: Ballroom CD

Recent advances in optimization and machine learning methods, it is now conceivable that the design of an individual treatment plan can be made with little, if any, human intervention. Adding autosegmentation processes to automated planning will result in dramatic increase in the efficiency and consistency of individual plans. Once the anatomic information, through imaging, is acquired for planning purposes, the majority of the steps required for the generation of the optimal plan could be automated. Such efforts are already being pursued at many institutions. However, since treatment plan design is one of the most important steps affecting the quality of a delivered treatment, human intervention, or at least supervision, will be crucial for the gradual development of confidence in these automated processes. In this talk, I will provide my insights on the aspects of automated treatment planning that would be addressed for this practice to become an integral part of the future practice of radiation therapy.

Learning Objectives:
1. Understand the concerns related to the implementation and practice of automated treatment planning from a clinician’s perspective.
2. Understand the impact of automated treatment planning on improving quality and and consistence of radiation therapy from a clinician’s perspective.

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