The Instant Plan

Radiation treatment planning — determining the beam angles, intensities, and leaf sequences that deliver prescribed dose to a tumor while sparing surrounding organs — takes minutes to hours. The process is iterative: propose a plan, evaluate the dose distribution, adjust, re-evaluate. Each evaluation requires solving a forward dose calculation; each adjustment requires human or algorithmic judgment. The iteration is the bottleneck.

AIRT replaces the iteration with inference. An end-to-end deep learning framework maps directly from CT images and anatomical contours to deliverable single-arc VMAT prostate plans — including leaf sequencing — in under one second on a single GPU. Trained on over 10,000 prostate cases with differentiable dose feedback and adversarial fluence shaping, the system achieves target homogeneity of HI = 0.10 plus or minus 0.01 and non-inferior organ-at-risk sparing compared to commercial planning systems.

The system doesn’t optimize a plan. It recognizes one. The deep network has seen thousands of plans and their associated anatomies; given a new anatomy, it predicts what the plan should look like without iterating through the space of possible plans.

The through-claim: the computational cost of treatment planning was never in the physics — it was in the search. Dose calculation is a forward problem with known physics. Finding the plan that produces the right dose distribution is an inverse problem traditionally solved by iterative search. Sub-second planning demonstrates that the inverse mapping from anatomy to plan is learnable — the search was exploring a structured space that a sufficiently trained model can traverse in a single forward pass. The minutes of iteration were the cost of not knowing the mapping, not the cost of the computation.