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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-CXR-VQA provides a large-scale LLM-enhanced dataset for visual question answering in medical chest x-ray images.

Published: Jan. 21, 2025. Version: 1.0.0

Database Contributor Review

ER-REASON: A Benchmark Dataset for LLM-Based Clinical Reasoning in the Emergency Room

Mel Molina, Nikita Mehandru, Niloufar Golchini, et al.

The ER-REASON dataset is a longitudinal collection of 25,174 de-identified clinical notes for 3,437 patients admitted to the emergency room (ER) at a large academic medical center between March 1, 2022, and March 31, 2024.

Published: Oct. 23, 2025. Version: 1.0.0

Database Credentialed Access

Medical Expert Annotations of Unsupported Facts in Doctor-Written and LLM-Generated Patient Summaries

Stefan Hegselmann, Shannon Shen, Florian Gierse, et al.

Annotations for unsupported facts in 100 original MIMIC patient summaries (discharge instructions) and hallucinations in 100 Large Language Model (LLM) generated patient summaries labeled by two medical experts.

Published: April 30, 2025. Version: 1.0.1

Database Credentialed Access

EHRNoteQA: An LLM Benchmark for Real-World Clinical Practice Using Discharge Summaries

Sunjun Kweon, Jiyoun Kim, Heeyoung Kwak, et al.

An LLM Benchmark for Real-World Clinical Practice Using Discharge Summaries

Published: June 26, 2024. Version: 1.0.1

Database Credentialed Access

MIMIC-III-Ext-VeriFact-BHC: Labeled Propositions From Brief Hospital Course Summaries for Long-form Clinical Text Evaluation

Philip Chung, Akshay Swaminathan, Alex Goodell, et al.

A clinician-labeled dataset for fact-checking long-form clinical text against patient EHRs. The dataset contains LLM-written and human-written Brief Hospital Course summaries decomposed to atomic claim and sentence propositions with annotations.

natural language processing clinical informatics formal logic chart review clinical notes text embedding electronic health records artificial intelligence brief hospital course long-form text llm evaluation logical atomism retrieval-augmented generation text reranking clinical medicine atomic claim fact verification hybrid retrieval llm-as-a-judge large language models

Published: Aug. 27, 2026. Version: 1.1.0

Database Credentialed Access

MIMIC-III-Ext-SBDH: An annotated social and behavioral determinants of health dataset

Zifan Gu, Lesi He, Donghan Yang

An annotated social and behavioral determinants of health dataset derived from clinical notes in the MIMIC-III database.

Published: Sept. 1, 2026. Version: 1.0.0

Database Credentialed Access

MIMIC-IV-Ext-MedicalBench: Evaluating Large Language Models Towards Improved Medical Concept Extraction

Zhichao Yang, Gregory Lyng, Sanjit Batra, et al.

This dataset is an evidence‑grounded benchmark built on MIMIC‑IV discharge summaries that evaluates how well large language models can verify ICD‑10 medical concepts, including implicitly documented diagnoses, by identifying supporting text evidence.

Published: March 23, 2026. 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.

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: Aug. 25, 2026. Version: 1.0.1

Database Credentialed Access

MIMIC-III-Ext-Notes

Darren Liu, Monique Bouvier, Delgersuren Bold, et al.

We evaluated general large language models' performance in clinical information extraction on MIMIC-III notes.

Published: Feb. 27, 2026. Version: 1.0.0

Database Credentialed Access

RadVLM Instruction Dataset

Nicolas Deperrois, Hidetoshi Matsuo, Samuel Ruiperez-Campillo, et al.

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