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---
language:
- en
pretty_name: "MedBrowseComp: Medical Browsing and Comparison Dataset"
tags:
- medical
- healthcare
- browsing
- comparison
license: "apache-2.0"
task_categories:
- question-answering
- text-retrieval
configs:
- config_name: default # Defines a configuration named 'default'
data_files: # Specifies data files for this configuration
- split: MedBrowseComp_50
path: MedBrowseComp_50.csv
- split: MedBrowseComp_605
path: MedBrowseComp_605.csv
- split: MedBrowseComp_CUA
path: MedBrowseComp_CUA.csv
---
# MedBrowseComp Dataset
This repository contains datasets for medical information-seeking-oriented deep research and computer use tasks.
## Datasets
The repository contains three harmonized datasets:
1. **MedBrowseComp_50**: A collection of 50 medical entries for browsing and comparison.
2. **MedBrowseComp_605**: A comprehensive collection of 605 medical entries.
3. **MedBrowseComp_CUA**: A curated collection of medical data for comparison and analysis.
## Usage
These datasets can be used for various medical text processing tasks, information retrieval, and comparative analysis.
Example usage with the Hugging Face datasets library:
```python
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("AIM-Harvard/MedBrowseComp")
# Access specific splits
med50_data = dataset["MedBrowseComp_50"]
med605_data = dataset["MedBrowseComp_605"]
cua_data = dataset["MedBrowseComp_CUA"]
```
## GitHub Repository
For more information and related tools, visit: [https://github.com/MedBrowseComp](https://github.com/MedBrowseComp)
## Citation
If you use this dataset in your research, please cite: https://arxiv.org/abs/2505.14963
```
@misc{chen2025medbrowsecompbenchmarkingmedicaldeep,
title={MedBrowseComp: Benchmarking Medical Deep Research and Computer Use},
author={Shan Chen and Pedro Moreira and Yuxin Xiao and Sam Schmidgall and Jeremy Warner and Hugo Aerts and Thomas Hartvigsen and Jack Gallifant and Danielle S. Bitterman},
year={2025},
eprint={2505.14963},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.14963},
}
```
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