Resources

20 results for "medical question answering"

Database Credentialed Access

Medical-CXR-VQA dataset: A Large-Scale LLM-Enhanced Medical Dataset for Visual Question Answering on Chest X-Ray Images

Xinyue Hu, Lin Gu, Kazuma Kobayashi, et al.

Medical Visual Question Answering (VQA) is an important task in medical multi-modal Large Language Models (LLMs), aiming to answer clinically relevant questions regarding input medical images. This technique has the potential to improve the efficien…
Published: Jan. 21, 2025. Version: 1.0.0
Database Credentialed Access

MIMIC-Ext-CXR-QBA: A Structured, Tagged, and Localized Visual Question Answering Dataset with Question-Box-Answer Triplets and Scene Graphs for Chest X-ray Images

Philip Müller, Friederike Jungmann, Georgios Kaissis, et al.

Visual Question Answering (VQA) enables flexible and context-dependent analysis of medical images, such as chest X-rays (CXRs), by allowing users to pose specific questions and receive nuanced answers. However, existing CXR VQA datasets are typicall…
Published: Aug. 25, 2026. Version: 1.0.1
Database Credentialed Access

Medical-Diff-VQA: A Large-Scale Medical Dataset for Difference Visual Question Answering on Chest X-Ray Images

Xinyue Hu, Lin Gu, Qiyuan An, et al.

The task of Difference Visual Question Answering involves answering questions about the difference between a pair of main and reference images. This process is consistent with the radiologist's diagnosis practice that compares the current image with…
Published: Feb. 3, 2025. Version: 1.0.1
Database Credentialed Access

RadQA: A Question Answering Dataset to Improve Comprehension of Radiology Reports

Sarvesh Soni, Kirk Roberts

We present a radiology question answering dataset, RadQA, with 3074 questions posed against radiology reports and annotated with their corresponding answer spans (resulting in a total of 6148 question-answer evidence pairs) by physicians. The questi…
Published: Dec. 9, 2022. Version: 1.0.0
Database Credentialed Access

DrugEHRQA: A Question Answering Dataset on Structured and Unstructured Electronic Health Records For Medicine Related Queries

Jayetri Bardhan, Anthony Colas, Kirk Roberts, et al.

Electronic Health Records (EHR) contain patient records, stored in structured tables as well as unstructured clinical notes. The information in structured and unstructured EHR records is not strictly disjoint: information may be duplicated, contradi…
Published: April 12, 2022. Version: 1.0.0
Database Credentialed Access

Annotated Question-Answer Pairs for Clinical Notes in the MIMIC-III Database

Xiang Yue, Xinliang Frederick Zhang, Huan Sun

Clinical question answering (QA) (or reading comprehension) aims to automatically answer questions from medical professionals based on clinical texts. We release this dataset, which contains 1287 annotated QA pairs on 36 sampled discharge summaries …
Published: Jan. 15, 2021. Version: 1.0.0
Database Open Access

Q-Pain: A Question Answering Dataset to Measure Social Bias in Pain Management

Cécile Logé, Emily Ross, David Yaw Amoah Dadey, et al.

We introduce Q-Pain, a dataset for assessing bias in medical QA in the context of pain management. We developed 55 medical question-answer pairs across five different types of pain management: each question includes a detailed patient-specific medic…
Published: June 11, 2021. Version: 1.0.0
Database Credentialed Access

EHRXQA: A Multi-Modal Question Answering Dataset for Electronic Health Records with Chest X-ray Images

Seongsu Bae, Daeun Kyung, Jaehee Ryu, et al.

Electronic Health Records (EHRs), which contain patients' medical histories in various multi-modal formats, often overlook the potential for joint reasoning across imaging and table modalities underexplored in current EHR Question Answering (QA) sys…
Published: July 23, 2024. Version: 1.0.0
Database Credentialed Access

MeDiSumQA: Patient-Oriented Question-Answer Generation from Discharge Letters

Amin Dada, Osman Alperen Koras, Marie Bauer, et al.

While increasing patients' access to medical documents improves medical care, this benefit is limited by varying health literacy levels and complex medical terminology. Large language models (LLMs) offer solutions by simplifying medical information.…
Published: May 5, 2025. Version: 1.0.0
Database Credentialed Access

MIMIC-Ext-MIMIC-CXR-VQA: A Complex, Diverse, And Large-Scale Visual Question Answering Dataset for Chest X-ray Images

Seongsu Bae, Daeun Kyung, Jaehee Ryu, et al.

We introduce MIMIC-Ext-MIMIC-CXR-VQA (i.e., Extended from MIMIC database), a complex, diverse, and large-scale dataset designed for Visual Question Answering (VQA) tasks within the medical domain, focusing primarily on chest radiographs. This datase…
Published: July 19, 2024. Version: 1.0.0