What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams
Open accessPreprint — published as 10.3390/app11146421 · Paper page
Abstract
Open domain question answering (OpenQA) tasks have been recently attracting more and more attention from the natural language processing (NLP) community. 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. We implement both rule-based and popular neural methods by sequentially combining a document retriever and a machine comprehension model. Through experiments, we find that even the current best method can only achieve 36.7%, 42.0%, and 70.1% of test accuracy on the English, traditional Chinese, and simplified Chinese questions, respectively. We expect MedQA to present great challenges to existing OpenQA systems and hope that it can serve as a platform to promote much stronger OpenQA models from the NLP community in the future.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
MedQA (USMLE) is a multiple-choice question dataset derived from medical licensing exams, focused on clinical knowledge assessment.
“- MedQA (USMLE): A multiple-choice question dataset derived from medical licensing exams, focused on clinical knowledge assessment [71].”
From Retrieval-Augmented Generation (RAG) in Healthcare: A Comprehensive Review (2025) · cites this paper as [71]
The citation lists haven’t been fetched yet
References 41 · Cited by 718
- Authors
- Jin, D., Pan, E., Oufattole, N., Weng, W.-H., Fang, H., & Szolovits, P.
- Affiliations
- Massachusetts Institute of Technology (US)
- Huazhong University of Science and Technology (CN)
- Corresponding author
- Jin, Di
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2021-05-21
- Volume
- 11
- Issue
- 14
- Pages
- 6421-6421
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W3162922479
- Cited by
- 718
- References
- 41
- Citations by year
- 2026: 285 · 2025: 241 · 2024: 120 · 2023: 51 · 2022: 12 · 2021: 9
- FWCI
- 16.96
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Applied Sciences (publishedVersion, cc-by)
- Preprints.org (acceptedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- open-domain question answering, medical question answering, multiple-choice questions, Chinese language, document retrieval, machine reading comprehension, disease diagnosis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
