Choosing when, where and how long to treat each cancer patient can be an arduous task that often results in a waste of potential treatment bandwidth. Gray automatically generates scheduling templates specific to your center, ensuring your patients get the care they deserve as quickly and stress-free as possible.
We understand that oncology departments are highly variable and that yours is no exception. Gray’s optimizer will learn how your center stands out, and adjust accordingly in order to optimize your treatment potential.
Nation leader in treating H&N? Perform more SBRT than any other hospital? Gray will account for that.
Scheduling appointments has always been such a chaotic task that the concept of accounting for patient preferences was impossible. No longer. Gray’s on-the-fly patient scheduling platform will explicitly account for your patient’s desires.
PhD Medical Physics, specialized in deep learning for outcome prediction in oncology.
PhD Medical Physics, specialized in optimization and radiation therapy treatment planning.
MSc machine learning, specialized in secure back-end development and deployment.
James McGill Professor. Director of the McGill University Medical Physics Unit
Full Professor, Polytechnique Montreal. Canada research chair in Healthcare Logistics.
Associate Professor, Polytechnique Montreal.