# Exploring the Capabilities and Limitations of Large Language Models for Radiation Oncology Decision Support

arXiv: https://arxiv.org/abs/2501.02346 · HTML: https://mlsanity.com/paper/2501.02346/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 45 references that are in the map.
- First posted on arXiv 4 Jan 2025.

## Builds on (most cited first, 9 of 9)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation](https://mlsanity.com/paper/1603.05959/) (2016)
- [Metrics reloaded: Recommendations for image analysis validation](https://mlsanity.com/paper/2206.01653/) (2022)
- [Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multi-Sequence MRI](https://mlsanity.com/paper/1903.07988/) (2019)
- [Deep learning for brain metastasis detection and segmentation in longitudinal MRI data](https://mlsanity.com/paper/2112.11833/) (2021)
- [Preoperative brain tumor imaging: models and software for segmentation and standardized reporting](https://mlsanity.com/paper/2204.14199/) (2022)
- [Multimodal Volume-Aware Detection and Segmentation for Brain Metastases Radiosurgery](https://mlsanity.com/paper/1908.05418/) (2019)
- [A Survey of Incremental Transfer Learning: Combining Peer-to-Peer Federated Learning and Domain Incremental Learning for Multicenter Collaboration](https://mlsanity.com/paper/2309.17192/) (2023)

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

- [Improving Generalization of Deep Learning for Brain Metastases Segmentation Across Institutions](https://mlsanity.com/paper/2604.00397/) (2026)

## Abstract (arXiv)

Thanks to the rapidly evolving integration of LLMs into decision-support tools, a significant transformation is happening across large-scale systems. Like other medical fields, the use of LLMs such as GPT-4 is gaining increasing interest in radiation oncology as well. An attempt to assess GPT-4's performance in radiation oncology was made via a dedicated 100-question examination on the highly specialized topic of radiation oncology physics, revealing GPT-4's superiority over other LLMs. GPT-4's performance on a broader field of clinical radiation oncology is further benchmarked by the ACR Radiation Oncology In-Training (TXIT) exam where GPT-4 achieved a high accuracy of 74.57%. Its performance on re-labelling structure names in accordance with the AAPM TG-263 report has also been benchmarked, achieving above 96% accuracies. Such studies shed light on the potential of LLMs in radiation oncology. As interest in the potential and constraints of LLMs in general healthcare applications continues to rise5, the capabilities and limitations of LLMs in radiation oncology decision support have not yet been fully explored.