README.md

---
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.

![Overall](medcomp.png)

## 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}, 
}
```