Datasets:
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---
language:
- en
license: cc-by-4.0
size_categories:
- 1K<n<10K
task_categories:
- visual-question-answering
- multiple-choice
- text-generation
pretty_name: TemMed-Bench
configs:
- config_name: Image Pair Selection
data_files:
- split: test
path: TestSet_ImagePairSelection.json
- config_name: VQA & Report Generation
data_files:
- split: test
path: TestSet_VQA_ReportGeneration.json
- config_name: VQA_Selected_2000
data_files:
- split: test
path: TestSet_SelectedVQA_2000.json
- config_name: TrainSet KnowledgeCorpus
data_files:
- split: train
path: TrainSet_KnowledgeCorpus.json
---
# TemMed-Bench: Evaluating Temporal Medical Image Reasoning in Vision-Language Models
[**🌐 Homepage**](https://temmedbench.github.io/) | [**🐱 Github**](https://github.com/Levi-ZJY/TemMed-Bench) | [**📖 Paper**](https://arxiv.org/abs/2509.25143)
## Intro
<img src="./misc/Teaser_Figure.png" width="750" />
TemMed-Bench features three primary highlights.
- **Temporal reasoning focus:** Each sample in TemMed-Bench includes historical condition information, which challenges models to analyze changes in patient conditions over time.
- **Multi-image input:** Each sample in TemMed-Bench contains multiple images from different visits as input, emphasizing the need for models to process and reason over multiple images.
- **Diverse task suite:** TemMed-Bench comprises three tasks, including VQA, report generation, and image-pair selection. Additionally, TemMed-Bench includes a knowledge corpus with more than 17,000 instances to support retrieval-augmented generation (RAG).
## Benchmark Overview
- **Examples of the three tasks in TemMed-Bench:**
<img src="./misc/Task_Figure.png" width="700" />
- **Key statistics of TemMed-Bench:**
<img src="./misc/Data_Amount.png" width="330" />
## Load Dataset
Please refer to [**🐱 Github**](https://github.com/Levi-ZJY/TemMed-Bench)
## Contact
* Junyi Zhang: JunyiZhang2002@g.ucla.edu
## Citation
```
@misc{zhang2025temmedbenchevaluatingtemporalmedical,
title={TemMed-Bench: Evaluating Temporal Medical Image Reasoning in Vision-Language Models},
author={Junyi Zhang and Jia-Chen Gu and Wenbo Hu and Yu Zhou and Robinson Piramuthu and Nanyun Peng},
year={2025},
eprint={2509.25143},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2509.25143},
}
```
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