Effective education in radiotherapy plan quality review requires a robust, regularly updated set of examples and the flexibility to demonstrate multiple possible planning approaches and their consequences. However, the current clinic-based paradigm does not support these needs. To address this, we have developed 'Virtual Dosimetrist' models that can both generate training examples of suboptimal treatment plans and then allow trainees to improve the plan quality through simple natural language prompts, as if communicating with a dosimetrist. The dose generation and modification process is accurate, rapid, and requires only modest resources. This work is the first to combine dose distribution prediction with natural language processing; providing a robust pipeline for both generating suboptimal training plans and allowing trainees to practice their critical plan review and improvement skills that addresses the challenges of the current clinic-based paradigm.
View on arXiv@article{gay2025_2505.09796,
title={ Virtual Dosimetrists: A Radiotherapy Training "Flight Simulator" },
author={ Skylar S. Gay and Tucker Netherton and Barbara Marquez and Raymond Mumme and Mary Gronberg and Brent Parker and Chelsea Pinnix and Sanjay Shete and Carlos Cardenas and Laurence Court },
journal={arXiv preprint arXiv:2505.09796},
year={ 2025 }
}