Resources


Database Credentialed Access

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

Sarvesh Soni, Kirk Roberts

RadQA is an electronic health record question answering dataset containing clinical questions that can be answered using the Findings and Impressions sections of radiology reports

machine reading comprehension radiology reports question answering clinical notes electronic health records

Published: Dec. 9, 2022. Version: 1.0.0

Database Restricted Access

LATTE-CXR: Locally Aligned TexT and imagE, Explainable dataset for Chest X-Rays

Elham Ghelichkhan, Tolga Tasdizen

This dataset includes bounding box-statement pairs for chest X-ray images, derived from radiologists’ eye-tracking data (for explainability) and annotations, for local visual-language models.

eye-tracking chest x-ray dataset automatically generated dataset caption-guided object detection image captioning with region-level description grounded radiology report generation phrase grounding xai multi-modal learning local visual-language models localization

Published: Feb. 4, 2025. Version: 1.0.0

Database Restricted Access

LATTE-CXR: Locally Aligned TexT and imagE, Explainable dataset for Chest X-Rays

Elham Ghelichkhan, Tolga Tasdizen

This dataset includes bounding box-statement pairs for chest X-ray images, derived from radiologists’ eye-tracking data (for explainability) and annotations, for local visual-language models.

eye-tracking chest x-ray dataset automatically generated dataset caption-guided object detection image captioning with region-level description grounded radiology report generation phrase grounding xai multi-modal learning local visual-language models localization

Published: Feb. 4, 2025. Version: 1.0.0

Database Credentialed Access

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

Sarvesh Soni, Kirk Roberts

RadQA is an electronic health record question answering dataset containing clinical questions that can be answered using the Findings and Impressions sections of radiology reports

machine reading comprehension radiology reports question answering clinical notes electronic health records

Published: Dec. 9, 2022. Version: 1.0.0

Database Credentialed Access

Establishment of a Chinese critical care database from electronic healthcare records in a tertiary care medical center

Senjun Jin, Lin Chen, Kun Chen, Zhongheng Zhang

Chinese critical care database from electronic healthcare records in a tertiary care medical center

china critical care database

Published: Jan. 19, 2023. Version: 1.0

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, Daniel Rueckert

We present a large-scale CXR VQA dataset derived from MIMIC-CXR with 42M QA pairs, featuring hierarchical answers, bounding boxes, and structured tags. We generated QA-pairs using LLM-based extraction from radiology reports and localization models.

chest x-rays vqa localization scene graphs

Published: July 22, 2025. Version: 1.0.0

Challenge Credentialed Access

ShAReCLEF eHealth Evaluation Lab 2014 (Task 2): Disorder Attributes in Clinical Reports

Danielle Mowery

The ShARe/CLEF eHealth 2014 Challenge (Task 2) on Disorder Attributes in Clinical Reports

Published: Nov. 1, 2013. Version: 1.0

Model Credentialed Access

Asclepius-R : Clinical Large Language Model Built On MIMIC-III Discharge Summaries

Sunjun Kweon, Junu Kim, Jiyoun Kim, Sujeong Im, Eunbyeol Cho, Seongsu Bae, Jungwoo Oh, Gyubok Lee, Jong Hak Moon, Seng Chan You, Seungjin Baek, Chang Hoon Han, Yoon Bin Jung, Yohan Jo, Edward Choi

Asclepius: Publicly Available Clinical Large Language Models with Synthetic Clinical Notes Asclepius-R: A instruction-finetuned large language model with MIMIC-III clinical notes

clinical notes synthetic clinical notes synthetic notes asclepius open-source llm clinical llm large language model

Published: March 25, 2024. Version: 1.1.0

Database Credentialed Access

RadVLM Instruction Dataset

Nicolas Deperrois, Hidetoshi Matsuo, Samuel Ruiperez-Campillo, Moritz Vandenhirtz, Sonia Laguna, Alain Ryser, Koji Fujimoto, Mizuho Nishio, Thomas Sutter, Julia Vogt, Jonas Kluckert, Thomas Frauenfelder, Christian Bluethgen, Farhad Nooralahzadeh, Michael Krauthammer

This dataset is designed to construct RadVLM, a vision–language model for chest X-ray interpretation. It includes instruction data for tasks such as report generation, abnormality detection, and region grounding, and multitask conversation.

chest x-rays vision-language models medical ai

Published: Sept. 25, 2025. Version: 1.0.0