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Credentialed Access
MedVAL-Bench: Expert-Annotated Medical Text Validation Benchmark
Asad Aali, Vasiliki Bikia, Maya Varma, et al.
MedVAL-Bench is the first large-scale physician-validated benchmark for medical text validation, spanning 6 diverse medical tasks and containing 840 language model-generated outputs annotated by 12 physicians with error assessments and risk grades.
Published: Nov. 14, 2025.
Version: 1.0.1
Database
Credentialed Access
Deidentified Medical Text
Margaret Douglass, Bill Long, George Moody, et al.
Gold standard corpus of 2,434 deidentified nursing notes
medical text
nursing notes
hipaa
de-identification
Published: Dec. 18, 2007.
Version: 1.0
Database
Credentialed Access
EHR-DS-QA: A Synthetic QA Dataset Derived from Medical Discharge Summaries for Enhanced Medical Information Retrieval Systems
Konstantin Kotschenreuther
Dataset consisting of question and answer pairs synthetically generated from medical discharge summaries, designed to facilitate the training and development of large language models specifically tailored for healthcare applications
mimic-iv
clinical question-answering
medical discharge summaries
large language models
Published: Jan. 11, 2024.
Version: 1.0.0
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.
artificial intelligence
natural language processing
clinical notes
electronic health records
brief hospital course
large language models
long-form text
chart review
text reranking
atomic claim
hybrid retrieval
clinical informatics
clinical medicine
fact verification
retrieval-augmented generation
logical atomism
text embedding
formal logic
llm-as-a-judge
llm evaluation
Published: April 9, 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
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
RuMedNLI: A Russian Natural Language Inference Dataset For The Clinical Domain
Pavel Blinov, Aleksandr Nesterov, Galina Zubkova, et al.
RuMedNLI is the full counterpart dataset of MedNLI in Russian language.
natural language inference
recognizing textual entailment
russian language
Published: April 1, 2022.
Version: 1.0.0
Database
Credentialed Access
FFA-IR: Towards an Explainable and Reliable Medical Report Generation Benchmark
Mingjie Li, Wenjia Cai, Rui Liu, et al.
Benchmark dataset for report generation based on fundus fluorescein angiography images and reports.
fundus fluorescein angiography
medical report generation
vision and language
explainable and reliable evaluation
Published: Jan. 21, 2025.
Version: 1.1.0
Software
Open Access
De-Identification Software Package
The deid software package includes code and dictionaries for automated location and removal of protected health information (PHI) in free text from medical records.
phi
deidentification
anonymization
Published: Dec. 18, 2007.
Version: 1.1
Database
Credentialed Access
Annotated Question-Answer Pairs for Clinical Notes in the MIMIC-III Database
Xiang Yue, Xinliang Frederick Zhang, Huan Sun
Annotated Question Answering Pairs for Clinical Notes in the MIMIC-III Database
clinical question answering
clinical nlp
clinical reading comprehension
Published: Jan. 15, 2021.
Version: 1.0.0
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Maintained by the MIT Laboratory for Computational Physiology
Supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB), National Heart Lung and Blood Institute (NHLBI), and NIH Office of the Director under NIH grant numbers U24EB037545 and R01EB030362
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