Resources


Database Credentialed Access

MedVH: Towards Systematic Evaluation of Hallucination for Large Vision Language Models in the Medical Context

Zishan Gu, Jiayuan Chen, Fenglin Liu, et al.

MedVH provides a visual hallucination evaluation benchmark for large language models in the medical context. It formulates tests using chest X-ray images, including multi-choice question answering and long-text generation tasks.

Published: Dec. 10, 2025. Version: 1.0.1

Database Credentialed Access

CXReasonBench: A Benchmark for Evaluating Structured Diagnostic Reasoning in Chest X-rays

Hyungyung Lee, Geon Choi, Jung Oh Lee, et al.

CheXStruct is an automated pipeline that derives structured diagnostic reasoning steps from chest X-rays. CXReasonBench builds on this to evaluate whether models perform clinically grounded, multi-step reasoning beyond final diagnoses.

evaluation chest x-ray benchmark structured chest x-ray qa intermediate reasoning steps structured reasoning grounded reasoning diagnostic reasoning structured diagnostic pipeline

Published: Oct. 23, 2025. Version: 1.0.1

Model Credentialed Access

RadVLM model

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

RadVLM is a 7B-parameter vision-language model fine-tuned on public chest-X-ray data that drafts reports, lists abnormalities, grounds findings, and chats about a CXR through a single image-to-text interface.

Published: Oct. 8, 2025. Version: 1.0.0

Database Restricted Access

Organ Retrieval and Collection of Health Information for Donation (ORCHID)

Hammaad Adam, Vinith Suriyakumar, Tom Pollard, et al.

Multi-center dataset on organ procurement in the United States

organ procurement organizations organ transplantation

Published: Sept. 29, 2025. Version: 2.1.1

Database Credentialed Access

Multimodal Clinical Monitoring in the Emergency Department (MC-MED)

Aman Kansal, Emma Chen, Tom Jin, et al.

A multimodal dataset of deidentified clinical and physiological data from emergency department visits, supporting research on patient outcomes, care processes, and the effects of continuous monitoring during and after the COVID-19 pandemic.

Published: Sept. 25, 2025. Version: 1.0.1

Database Credentialed Access

FDTooth: Intraoral Photographs and Cone-Beam Computed Tomography Images for Fenestration and Dehiscence Detection

Yanqi Yang, Xiaomeng LI, Keyuan Liu, et al.

FDTooth is a dataset containing intraoral photographs and cone-beam computed tomography (CBCT) images with annotations for automated detection of fenestration and dehiscence in anterior teeth.

Published: May 5, 2025. 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.

MeDiSumQA is a dataset of patient-oriented QA pairs from MIMIC-IV discharge summaries, designed to evaluate LLMs in generating safe, patient-friendly medical responses for clinical QA and healthcare communication.

Published: May 5, 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

Challenge Credentialed Access

CXR-LT: Multi-Label Long-Tailed Classification on Chest X-Rays

Gregory Holste, Mingquan Lin, Song Wang, et al.

CXR-LT 2024 was a challenge for long-tailed, multi-label, and zero-shot thorax disease classification on chest X-rays, held at MICCAI 2024. This page contains long-tailed labels for 45 diseases from the CXR-LT 2024 and 2023 challenges.

disease classification chest x-ray computer-aided diagnosis long-tailed learning cardiopulmonary disease zero-shot learning deep learning artificial intelligence

Published: March 19, 2025. Version: 2.0.0

Database Open Access

Synthetic Mention Corpora for Disease Entity Recognition and Normalization

Kuleen Sasse, John David Osborne

We present the Synthetic Mention Corpora for Disease Entity Recognition and Normalization, containing 128000 disease mentions from the UMLS disorder group, generated by an LLM. This corpus aims to improve these tasks in biomedical and clinical texts.

nlp named entity recognition data augmentation entity normalization machine learning

Published: Feb. 3, 2025. Version: 1.0.0