OBJECTIVES: This study evaluated the effect of enhancing a GPT-4 model with retrieval-augmented generation on its ability to diagnose and classify traumatic injuries based on radiology reports.
BACKGROUND: The rapid development of large language models (LLMs) opens up new possibilities for the automated processing of medical texts. Transforming unstructured radiology reports into structured data is crucial for efficient use in clinical deci...
International journal of computer assisted radiology and surgery
Feb 3, 2025
Purpose Federated training is often challenging on heterogeneous datasets due to divergent data storage options, inconsistent naming schemes, varied annotation procedures, and disparities in label quality. This is particularly evident in the emerging...
AJR. American journal of roentgenology
Jan 29, 2025
Automated extraction of actionable details of recommendations for additional imaging (RAIs) from radiology reports could facilitate tracking and timely completion of clinically necessary RAIs and thereby potentially reduce diagnostic delays. The pu...
IEEE journal of translational engineering in health and medicine
Jan 28, 2025
The high volume of emergency room patients often necessitates head CT examinations to rule out ischemic, hemorrhagic, or other organic pathologies. A system that enhances the diagnostic efficacy of head CT imaging in emergency settings through struct...
PURPOSE: HER2 expression is crucial for the application of HER2-targeted antibody-drug conjugates. This study aims to construct a predictive model by integrating multiparametric magnetic resonance imaging (mpMRI) based multimodal radiomics and the Ve...
International journal of computer assisted radiology and surgery
Jan 8, 2025
PURPOSE: Systems equipped with natural language (NLP) processing can reduce missed radiological findings by physicians, but the annotation costs are burden in the development. This study aimed to compare the effects of active learning (AL) algorithms...
RATIONALE AND OBJECTIVES: Training Convolutional Neural Networks (CNN) requires large datasets with labeled data, which can be very labor-intensive to prepare. Radiology reports contain a lot of potentially useful information for such tasks. However,...
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jan 4, 2025
With the increasing popularity of medical imaging and its expanding applications, posing significant challenges for radiologists. Radiologists need to spend substantial time and effort to review images and manually writing reports every day. To addre...
BACKGROUND: Using artificial intelligence (AI) to augment knowledge is key to establishing precision education in modern radiology training. Our department has developed a novel AI-derived knowledge recommender, the first reported precision education...
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