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Artificial intelligence (AI) and machine learning (ML) approaches are transforming radiation oncology by facilitating automation and optimizing workflows for efficient high quality radiation therapy administration.  Examples of emerging AI technologies influencing healthcare can be found across the entire care trajectory.  In particular, AI applications across the radiation treatment pathway including those found in QA and QC processes, CT and MR simulation, multimodal image fusion, automatic segmentation, synthetic CT generation, treatment planning, online and offline adaptation and image guided radiation therapy will significantly affect how Radiation Therapists work and the decisions they make.  
Although the efficiencies afforded by automation are appealing to Radiation therapists and promise a focus on higher level tasks and patient care, there are risks and concerns that should not be ignored.  This talk will focus on the risks of automation from a Radiation therapists’ perspective and potential solutions for consideration.
The importance of AI and ML curriculum for undergraduate Radiation therapy education, participation in activities prior to and in preparation for AI clinical implementation to ensure safe and clinically relevant utilization, performing end to end testing with the multidisciplinary team, providing training and education for colleagues, understanding and performing regular QA processes and upkeeping fundamental radiation therapy domain knowledge will be discussed.  The role of advanced practice, and Clinical / Application specialists will also be considered.
Automation is inevitable.  How Radiation therapists respond is critical.