Resources


Database Credentialed Access

Chest X-ray segmentation images based on MIMIC-CXR

Li-Ching Chen, Po-Chih Kuo, Ryan Wang, et al.

A chest x-rays segmentation dataset derived from MIMIC-CXR based on deep learning algorithm and human examination.

segmentation chest x-rays cxr

Published: Aug. 18, 2022. 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

Database Credentialed Access

RaDialog Instruct Dataset

Chantal Pellegrini, Ege Özsoy, Benjamin Busam, et al.

Image-based instruct data for Chest X-Ray understanding and analysis.

medical image understaning radiology chatbot radiology report generation radiology assistant large vision-language models

Published: July 12, 2024. Version: 1.1.0

Database Credentialed Access

MIMIC-IV-Ext Triage Instruction Corpus

Qingyang Shen, Quan Guo

MIMIC-IV-Ext Triage Instruction Corpus includes 9,629 ED triage cases organized by the five-level ESI, enabling LLMs to improve triage accuracy. It provides CSV data, generation prompts, expert validation samples, and SQL QC scripts.

nlp clinical decision support machine learning large language models emergency severity index emergency triage

Published: March 4, 2025. Version: 1.0.0

Database Open Access

ReXErr-v1: Clinically Meaningful Chest X-Ray Report Errors Derived from MIMIC-CXR

Vishwanatha Rao, Serena Zhang, Julian Acosta, et al.

Chest X-Ray reports containing synthetic errors based upon the MIMIC-CXR database. Errors were injected using LLMs and sampled across common human and AI model errors.

Published: March 19, 2025. Version: 1.0.0

Database Open Access

Heart and lung segmentations for MIMIC-CXR/MIMIC-CXR-JPG and Montgomery County TB databases

Benjamin Duvieusart, Felix Krones, Guy Parsons, et al.

Heart and lung segmentations for 200 MIMIC-CXR/MIMIC-CXR-JPG chest x-rays and heart segmentations for 138 Montgomery County tuberculosis chest X-rays.

segmentation heart and lungs montgomery country tb mimic-cxr

Published: Aug. 14, 2023. Version: 1.0.0

Model Credentialed Access

Shareable Artificial Intelligence to Extract Cancer Outcomes from Electronic Health Records for Precision Oncology Research

Kenneth Kehl, Pavel Trukhanov, Christopher Fong, et al.

The DFCI-imaging-student and DFCI-medonc-student AI models for extracting cancer outcomes from imaging reports and medical oncologist notes from electronic health records.

Published: Oct. 24, 2024. Version: 1.0.0

Database Credentialed Access

MIMIC-IV-Ext-Instr: A Dataset of 450K+ EHR-Grounded Instruction-Following Examples

Zhenbang Wu, Anant Dadu, Mike Nalls, et al.

This dataset contains 450K open-ended instruction-following examples generated using GPT-3.5 based on the MIMIC-IV EHR database.

large language models medical question answering instruction tuning

Published: Sept. 9, 2025. Version: 1.0.0

Database Restricted Access

EchoNext: A Dataset for Detecting Echocardiogram-Confirmed Structural Heart Disease from ECGs

Pierre Elias, Joshua Finer

EchoNext is a curated dataset of electrocardiograms (ECGs) paired with echocardiogram-confirmed structural heart disease labels, designed to support the development and validation of machine learning models.

heart failure clinical decision support artificial intelligence health equity ecg machine learning deep learning electrocardiogram aortic stenosis cardiovascular screening valvular heart disease digital health ai model deployment left ventricular dysfunction ai in healthcare population health transthoracic echocardiogram structural heart disease

Published: Sept. 16, 2025. Version: 1.1.0

Database Restricted Access

EchoNext: A Dataset for Detecting Echocardiogram-Confirmed Structural Heart Disease from ECGs

Pierre Elias, Joshua Finer

EchoNext is a curated dataset of electrocardiograms (ECGs) paired with echocardiogram-confirmed structural heart disease labels, designed to support the development and validation of machine learning models.

heart failure clinical decision support artificial intelligence health equity ecg machine learning deep learning electrocardiogram aortic stenosis cardiovascular screening valvular heart disease digital health ai model deployment left ventricular dysfunction ai in healthcare population health transthoracic echocardiogram structural heart disease

Published: Sept. 16, 2025. Version: 1.1.0