---
task_categories:
- question-answering
language:
- en
- he
- ja
- es
- pt
tags:
- medical
size_categories:
- n<1K
---

# WorldMedQA-V: A Multilingual, Multimodal Medical Examination Dataset
  <img src="src/logo.png" alt="logo" width="200"/>
  
## Overview

**WorldMedQA-V** is a multilingual and multimodal benchmarking dataset designed to evaluate vision-language models (VLMs) in healthcare contexts. The dataset includes medical examination questions from four countries—Brazil, Israel, Japan, and Spain—in both their original languages and English translations. Each multiple-choice question is paired with a corresponding medical image, enabling the evaluation of VLMs on multimodal data.

**Key Features:**
- **Multilingual:** Supports local languages (Portuguese, Hebrew, Japanese, and Spanish) as well as English translations.
- **Multimodal:** Each question is accompanied by a medical image, allowing for a comprehensive assessment of VLMs' performance on both textual and visual inputs.
- **Clinically Validated:** All questions and answers have been reviewed and validated by native-speaking clinicians from the respective countries.

## Dataset Details

- **Number of Questions:** 568
- **Countries Covered:** Brazil, Israel, Japan, Spain
- **Languages:** Portuguese, Hebrew, Japanese, Spanish, and English
- **Types of Data:** Multiple-choice questions with medical images
- **Evaluation:** Performance of models in both local languages and English, with and without medical images

The dataset aims to bridge the gap between real-world healthcare settings and AI evaluations, fostering more equitable, effective, and representative applications.

## Data Structure

The dataset is provided in TSV format, with the following structure:
- **ID**: Unique identifier for each question.
- **Question**: The medical multiple-choice question in the local language.
- **Options**: List of possible answers (A-D).
- **Correct Answer**: The correct answer's label.
- **Image Path**: Path to the corresponding medical image (if applicable).
- **Language**: The language of the question (original or English translation).

### Example from Brazil:

- **Question**: Um paciente do sexo masculino, 55 anos de idade, tabagista 60 maços/ano... [Full medical question see below]
- **Options**:
  - A: Aspergilose pulmonar
  - B: Carcinoma pulmonar
  - C: Tuberculose cavitária
  - D: Bronquiectasia com infecção
- **Correct Answer**: B
  <img src="src/example.png" alt="example" width="800"/>
  

### Evaluate models/results:

  <img src="src/results.png" alt="results" width="800"/>


## Download and Usage

The dataset can be downloaded from [Hugging Face datasets page](https://huggingface.co/datasets/WorldMedQA/V). All code for handling and evaluating the dataset is available in the following repositories:
- **Dataset Code**: [WorldMedQA GitHub repository](https://github.com/WorldMedQA/V)
- **Evaluation Code**: [VLMEvalKit GitHub repository](https://github.com/WorldMedQA/VLMEvalKit/tree/main)
  
**Where and How to start?**: [Google Colab Demo](https://colab.research.google.com/drive/16bw_7_sUTajNRZFunRNo3wqnL_tQWk6O)

## Citation

Please cite this dataset using our arXiv preprint:

```bibtex
@misc{WorldMedQA-V2024,
      title={WorldMedQA-V: a multilingual, multimodal medical examination dataset for multimodal language models evaluation}, 
      author={João Matos and Shan Chen and Siena Placino and Yingya Li and Juan Carlos Climent Pardo and Daphna Idan and Takeshi Tohyama and David Restrepo and Luis F. Nakayama and Jose M. M. Pascual-Leone and Guergana Savova and Hugo Aerts and Leo A. Celi and A. Ian Wong and Danielle S. Bitterman and Jack Gallifant},
      year={2024},
      eprint={2410.12722},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2410.12722}, 
}