

# Dataset Card for MedQA

## Dataset Description

- **Homepage:** https://github.com/jind11/MedQA
- **Pubmed:** False
- **Public:** True
- **Tasks:** QA


In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA,
collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and
traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively. Together
with the question data, we also collect and release a large-scale corpus from medical textbooks from which the reading
comprehension models can obtain necessary knowledge for answering the questions.



## Citation Information

```
@article{jin2021disease,
  title={What disease does this patient have? a large-scale open domain question answering dataset from medical exams},
  author={Jin, Di and Pan, Eileen and Oufattole, Nassim and Weng, Wei-Hung and Fang, Hanyi and Szolovits, Peter},
  journal={Applied Sciences},
  volume={11},
  number={14},
  pages={6421},
  year={2021},
  publisher={MDPI}
}

```

multilinguality:multilingual language:en language:zh license:unknown region:us
{"language": ["en", "zh"], "license": "unknown", "multilinguality": "multilingual", "pretty_name": "MedQA", "bigbio_language": ["English", "Chinese (Simplified)", "Chinese (Traditional, Taiwan)"], "bigbio_license_shortname": "UNKNOWN", "homepage": "https://github.com/jind11/MedQA", "bigbio_pubmed": false, "bigbio_public": true, "bigbio_tasks": ["QUESTION_ANSWERING"]}
TAGS
#multilinguality-multilingual #language-English #language-Chinese #license-unknown #region-us 



# Dataset Card for MedQA

## Dataset Description

- Homepage: URL
- Pubmed: False
- Public: True
- Tasks: QA


In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA,
collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and
traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively. Together
with the question data, we also collect and release a large-scale corpus from medical textbooks from which the reading
comprehension models can obtain necessary knowledge for answering the questions.







# Dataset Card for MedQA ## Dataset Description

- Homepage: URL
- Pubmed: False
- Public: True
- Tasks: QA


In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA,
collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and
traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively. Together
with the question data, we also collect and release a large-scale corpus from medical textbooks from which the reading
comprehension models can obtain necessary knowledge for answering the questions.
TAGS
#multilinguality-multilingual #language-English #language-Chinese #license-unknown #region-us 
 # Dataset Card for MedQA ## Dataset Description

- Homepage: URL
- Pubmed: False
- Public: True
- Tasks: QA


In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA,
collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and
traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively. Together
with the question data, we also collect and release a large-scale corpus from medical textbooks from which the reading
comprehension models can obtain necessary knowledge for answering the questions.
passage: TAGS
#multilinguality-multilingual #language-English #language-Chinese #license-unknown #region-us 
# Dataset Card for MedQA## Dataset Description

- Homepage: URL
- Pubmed: False
- Public: True
- Tasks: QA


In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA,
collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and
traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively. Together
with the question data, we also collect and release a large-scale corpus from medical textbooks from which the reading
comprehension models can obtain necessary knowledge for answering the questions.