The Radiology Gamuts Ontology (RGO)-an ontology of diseases, interventions, and imaging findings-was developed to aid in decision support, education, and translational research in diagnostic radiology. The ontology defines a subsumption (is_a) relati...
BMC medical informatics and decision making
Jan 22, 2016
BACKGROUND: In radiology, a vast amount of diverse data is generated, and unstructured reporting is standard. Hence, much useful information is trapped in free-text form, and often lost in translation and transmission. One relevant source of free-tex...
AMIA ... Annual Symposium proceedings. AMIA Symposium
Nov 5, 2015
We study machine learning techniques to automatically identify limb abnormalities (including fractures, dislocations and foreign bodies) from radiology reports. For patients presenting to the Emergency Room (ER) with suspected limb abnormalities (e.g...
BACKGROUND: Accumulating quantitative outcome parameters may contribute to constructing a healthcare organization in which outcomes of clinical procedures are reproducible and predictable. In imaging studies, measurements are the principal category o...
OBJECTIVE: To standardize and objectivize treatment response assessment in oncology, guidelines have been proposed that are driven by radiological measurements, which are typically communicated in free-text reports defying automated processing. We st...
Retrieval-augmented generation (RAG) is a strategy to improve the performance of large language models (LLMs) by providing an LLM with an updated corpus of knowledge that can be used for answer generation in real time. RAG may improve LLM performance...
Radiographics : a review publication of the Radiological Society of North America, Inc
Apr 1, 2025
Large language models (LLMs) such as generative pretrained transformers (GPTs) have had a major impact on society, and there is increasing interest in using these models for applications in medicine and radiology. This article presents techniques to ...
BACKGROUND: Applications of artificial intelligence (AI) in medicine are expanding every year. AI education is crucial to its appropriate use in healthcare; however, most US medical schools lack a dedicated AI curriculum. These resources are sparse f...
Recent advances in artificial intelligence have witnessed the emergence of large-scale deep learning models capable of interpreting and generating both textual and imaging data. Such models, typically referred to as foundation models (FMs), are train...