StanfordAIMI/GREEN-V2

---
license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- medical
size_categories:
- 100K<n<1M
---
# GREEN Dataset

We share the dataset used to train the LLM metric introduced in ["GREEN: Generative Radiology Report Evaluation and Error Notation"](https://arxiv.org/pdf/2405.03595).
GREEN is a evaluation metric for radiology reports that uses language models to identify and explain clinically significant errors, offering better alignment with expert preferences and more interpretable results compared to existing metrics. The method provides both quantitative scores and qualitative explanations, has been validated against expert assessments and GPT-4, and offers an open-source alternative that performs comparably to commercial solutions.
## Dataset Details

For details, please check the paper, [project page](https://stanford-aimi.github.io/green.html), [Github](https://github.com/Stanford-AIMI/GREEN)

### Dataset Description

- **Curated by:** [JB Delbrouk, Zhihong Chen, Sophie Ostmeier]
- **Funded by [optional]:** Stanford AIMI Center, German Research Foundation
- **Language(s) (NLP):** English
- **License:** Apache license 2.0
-
### Out-of-Scope Use

The dataset includes 50k examples that are not Chest Xray reports. However, currently the model and the dataset are primarily designed to be used for Chest Xrays.

## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**
```
@article{ostmeier2024green,
title={GREEN: Generative Radiology Report Evaluation and Error Notation},
author={Ostmeier, Sophie and Xu, Justin and Chen, Zhihong and Varma, Maya and Blankemeier, Louis and Bluethgen, Christian and Michalson, Arne Edward and Moseley, Michael and Langlotz, Curtis and Chaudhari, Akshay S and others},
journal={arXiv preprint arXiv:2405.03595},
year={2024}
}
```