arXiv:2603.09448v2 Announce Type: replace-cross Abstract: Delineating the clinical target volume (CTV) in radiotherapy involves complex margins constrained by tumor location and anatomical barriers. While deep learning models automate this process, their rigid reliance on expert-annotated data requires costly retraining whenever clinical guidelines update. To overcome this limitation, we introduce OncoAgent, a novel guideline-aware AI agent framework that seamlessly converts textual clinical guidelines into three-dimensional target contours in a training-free manner. Evaluated on esophageal ca
The increasing sophistication of large language models and multi-modal AI makes it possible to translate complex, unstructured textual guidelines into actionable, precise outputs for domains like medical imaging.
This development allows for a significant acceleration and cost reduction in the deployment of AI in critical medical applications, bypassing the traditional bottlenecks of expert data annotation and retraining with every guideline update.
AI models can now adapt to evolving clinical standards without extensive and expensive manual re-annotation, enabling broader and faster integration into healthcare workflows.
- · Healthcare providers
- · AI-driven diagnostic companies
- · Patients
- · Radiotherapy equipment manufacturers
- · Manual annotation services
- · Legacy medical imaging software
Radiotherapy planning becomes more efficient and consistent, reducing human error and improving patient outcomes.
The cost of developing and maintaining AI in medical imaging decreases dramatically, encouraging wider adoption across various specialties.
This paradigm shift could lead to a democratization of advanced medical AI, making high-quality diagnostics and treatment planning accessible in resource-limited settings worldwide.
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Read at arXiv cs.AI