Resources

Electronic Health Records (EHRs) are integral for storing comprehensive patient medical records, combining structured data (e.g., medications) with detailed clinical notes (e.g., physician notes). These elements are essential for straightforward dat…

The OpenOximetry Repository is a structured database designed to store clinical and laboratory pulse oximetry data and allows for consolidation of data sets held by collaborating organizations. Matched or independent readings of oxygen saturations, …

MIMIC-CXR is a large, open source dataset that is widely-used in medical AI research. One of the limitations of this dataset is the lack of ground truth labels for the chest X-ray studies. Prior work has extracted structured labels from the MIMIC-CX…

Databases that link molecular data to clinical outcomes can inform precision cancer research into novel prognostic and predictive biomarkers. However, outside of clinical trials, cancer outcomes are typically recorded only in text form within electr…

Medical decisions directly impact individuals’ health and well-being. Extracting decision spans from clinical notes plays a crucial role in understanding medical decision-making processes. The MedDec dataset includes expert-annotated decisions of te…

RadGraph2 is a dataset of 800 chest radiology reports annotated using a fine-grained entity-relationship schema, which is an expanded version of the previously introduced RadGraph dataset. In contrast with the previous approaches and the original Ra…

The Radiology Report Generation Models Evaluation Dataset For Chest X-rays (RadEvalX) is publicly available and developed similarly to the ReXVal dataset. Just like ReXVal, RadEvalX focuses on radiologist evaluations of errors found in automatically…

Recent advancements in large language models (LLMs) such as ChatGPT and LLaMA have hinted at their potential to revolutionize medical applications, yet their application in clinical settings often reveals limitations due to a lack of specialized tra…

Existing chest radiograph datasets, such as CheXpert and ChestX-ray14, have driven the development of new machine learning approaches to achieve expert or near-expert level performance on a variety of tasks. The primary focus of models developed usi…

This dataset was designed and created to enable advancements in healthcare-focused large language models, particularly in the context of retrieval-augmented clinical question-answering capabilities. Developed using a self-constructed pipeline based …