Resources

Emergency department (ED) patients often present with undiagnosed complaints, and can exhibit rapidly evolving physiology. Therefore, data from continuous physiologic monitoring, in addition to the electronic health record, is essential to understan…

FDTooth is a comprehensive dataset designed for the automated detection of fenestration and dehiscence (FD) in anterior teeth, combining intraoral photographs and corresponding cone-beam computed tomography (CBCT) images from 241 patients aged 9 to …

While increasing patients' access to medical documents improves medical care, this benefit is limited by varying health literacy levels and complex medical terminology. Large language models (LLMs) offer solutions by simplifying medical information.…

Large language models in healthcare can generate informative patient summaries while reducing the documentation workload of healthcare professionals. However, these models are prone to producing hallucinations, that is, generating unsupported inform…

Chest radiography presents a "long-tailed" distribution of findings, where a few diseases are common, but most are rare. Diagnosis is further complicated by its multi-label nature, as patients often exhibit multiple co-occurring findings. While rece…

Named Entity Recognition (NER) and Entity Normalization (EN) are fundamental tasks in information extraction, particularly in the biomedical and clinical domains. NER identifies textual mentions of entities, while EN maps these mentions to unique id…

CXRGraph is a dataset of structured radiology reports dataset following the RadGraph format, which has been tailored for the Automatic Radiology Report Generation (ARRG) task. CXRGraph assorts clinical information from full-text radiology reports in…

Automatic medical report generation (MRG) towards describing life-threatening lesions from given medical images, such as Chest X-ray and Fundus Fluorescein Angiography (FFA), has been a long-standing research topic in machine learning and automatic …

Large language models (LLMs) have recently demonstrated remarkable capabilities across a broad spectrum of tasks and applications, including the medical field. Models like GPT-4 excel in medical question answering but encounter challenges in interpr…

Surface electromyography (sEMG) signals reflect spinal motor neuron activities and can be used as intuitive inputs for human-machine interaction (HMI) via movement intent recognition. The motor neuron potentials of far-field (wrist) and near-field (…