# Demo: Generative AI helps Radiotherapy Planning with User Preference

DevFeed: [Demo: Generative AI helps Radiotherapy Planning with User Preference](<https://devfeed.tech/articles/demo-generative-ai-helps-radiotherapy-planning-with-user-preference-99381.md>)

Original publisher: [Read original article](<https://arxiv.org/abs/2512.08996>)

Author: Riqiang Gao, Simon Arberet, Martin Kraus, Han Liu, Wilko F. A. R. Verbakel, Dorin Comaniciu, Florin-Cristian Ghesu, Ali Kamen

Published: 2026-10-26T04:00:00Z

Content type: article

Language: en

Sources: [arXiv: Artificial Intelligence](<https://devfeed.tech/sources/arxiv-artificial-intelligence.md>), [arXiv: Machine Learning](<https://devfeed.tech/sources/arxiv-machine-learning.md>)

Topics: [generative-ai-tools](<https://devfeed.tech/topics/generative-ai-tools.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [personalization](<https://devfeed.tech/topics/personalization.md>)

Tags: [3d](<https://devfeed.tech/tags/3d.md>), [ai](<https://devfeed.tech/tags/ai.md>), [cs-ai](<https://devfeed.tech/tags/cs-ai.md>), [cs-cv](<https://devfeed.tech/tags/cs-cv.md>), [cs-lg](<https://devfeed.tech/tags/cs-lg.md>), [customizable](<https://devfeed.tech/tags/customizable.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [distributions](<https://devfeed.tech/tags/distributions.md>), [flexibility](<https://devfeed.tech/tags/flexibility.md>), [generative](<https://devfeed.tech/tags/generative.md>), [generative-ai](<https://devfeed.tech/tags/generative-ai.md>), [integration](<https://devfeed.tech/tags/integration.md>), [model](<https://devfeed.tech/tags/model.md>), [personalization](<https://devfeed.tech/tags/personalization.md>), [planning](<https://devfeed.tech/tags/planning.md>), [quality](<https://devfeed.tech/tags/quality.md>), [target](<https://devfeed.tech/tags/target.md>), [trade](<https://devfeed.tech/tags/trade.md>), [training](<https://devfeed.tech/tags/training.md>)

## AI overview

The proposed generative model predicts 3D radiotherapy dose distributions from user-defined preference flavors rather than reference plans used as training ground truth. Planners can adjust trade-offs between organs at risk (OARs) and planning target volumes (PTVs), allowing plans to reflect different preferences. The abstract says the method is designed to integrate with clinical treatment planning systems and reports better adaptability and plan quality than Varian RapidPlan in some scenarios; it does not provide evaluation details or establish superiority across all cases.

## Source excerpt

arXiv:2512.08996v3 Announce Type: replace-cross Abstract: Radiotherapy planning is a highly complex process that often varies significantly across institutions and individual planners. Most existing deep learning approaches for 3D dose prediction rely on reference plans as ground truth during training, which can inadvertently bias models toward specific planning styles or institutional preferences. In this study, we introduce a novel generative model that predicts 3D dose distributions based solely on user-defined preference flavors. These customizable preferences enable planners to prioritize specific trade-offs between organs-at-risk (OARs) and planning target volumes (PTVs), offering greater flexibility and personalization. Designed for seamless integration with clinical treatment planning systems, our approach assists users in generating high-quality plans efficiently. Comparative evaluations demonstrate that our method can surpasses the Varian RapidPlan model in both adaptability and plan quality in some scenarios.