# AI End-to-End Radiation Treatment Planning Under One Second

arXiv: https://arxiv.org/abs/2603.06338 · HTML: https://mlsanity.com/paper/2603.06338/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 29 references that are in the map.
- First posted on arXiv 6 Mar 2026.

## Builds on (most cited first, 6 of 6)

- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation](https://mlsanity.com/paper/2303.09975/) (2023)
- [Multi-Agent Reinforcement Learning Meets Leaf Sequencing in Radiotherapy](https://mlsanity.com/paper/2406.01853/) (2024)
- [A Beam's Eye View to Fluence Maps 3D Network for Ultra Fast VMAT Radiotherapy Planning](https://mlsanity.com/paper/2502.03360/) (2025)
- [FluenceFormer: Transformer-Driven Multi-Beam Fluence Map Regression for Radiotherapy Planning](https://mlsanity.com/paper/2512.22425/) (2025)
- [Demo: Generative AI helps Radiotherapy Planning with User Preference](https://mlsanity.com/paper/2512.08996/) (2025)

## Built on by (most cited first, 1 of 1)

- [Learning to Optimize Radiotherapy Plans via Fluence Maps Diffusion Model Generation and LSTM-based Optimization](https://mlsanity.com/paper/2605.13713/) (2026)

## Abstract (arXiv)

Artificial intelligence-based radiation therapy (RT) planning has the potential to reduce planning time and inter-planner variability, improving efficiency and consistency in clinical workflows. Most existing automated approaches rely on multiple dose evaluations and corrections, resulting in plan generation times of several minutes. We introduce AIRT (Artificial Intelligence-based Radiotherapy), an end-to-end deep-learning framework that directly infers deliverable treatment plans from CT images and structure contours. AIRT generates single-arc VMAT prostate plans, from imaging and anatomical inputs to leaf sequencing, in under one second on a single Nvidia A100 GPU. The framework includes a differentiable dose feedback, an adversarial fluence map shaping, and a plan generation augmentation to improve plan quality and robustness. The model was trained on more than 10,000 intact prostate cases. Non-inferiority to RapidPlan Eclipse was demonstrated across target coverage and OAR sparing metrics. Target homogeneity (HI = 0.10 ± 0.01) and OAR sparing were similar to reference plans when evaluated using AcurosXB. These results represent a significant step toward ultra-fast standardized RT planning and a streamlined clinical workflow.