Datasets:
File size: 3,196 Bytes
7bf5388 65df58c 4558f17 7bf5388 16b792d 7bf5388 d438f06 7bf5388 16b792d 7bf5388 16b792d 7bf5388 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 |
---
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
![example](src/example.png)
### Evaluate models/results:
![results](src/results.png)
## 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)
## Citation
Please cite this dataset as follows:
```bibtex
@article{WorldMedQA-V2024,
title={WorldMedQA-V: A Multilingual, Multimodal Medical Examination Dataset},
author={Matos and Chen et al.},
journal={Preprint},
year={2024},
}
|