AIMC Topic: Radiology Information Systems
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Use of ChatGPT Large Language Models to Extract Details of Recommendations for Additional Imaging From Free-Text Impressions of Radiology Reports.
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...
Multi-Branch CNN-LSTM Fusion Network-Driven System With BERT Semantic Evaluator for Radiology Reporting in Emergency Head CTs.
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...
Noninvasive identification of HER2 status by integrating multiparametric MRI-based radiomics model with the vesical imaging-reporting and data system (VI-RADS) score in bladder urothelial carcinoma.
Abdominal radiology (New York)
Jan 9, 2025
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...
Comparison of active learning algorithms in classifying head computed tomography reports using bidirectional encoder representations from transformers.
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...
Open-source Large Language Models can Generate Labels from Radiology Reports for Training Convolutional Neural Networks.
Academic radiology
Jan 6, 2025
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,...
Automatic medical report generation based on deep learning: A state of the art survey.
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...
RADHawk-an AI-based knowledge recommender to support precision education, improve reporting productivity, and reduce cognitive load.
Pediatric radiology
Dec 7, 2024
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...
Inclusive AI for radiology: Optimising ChatGPT-4 with advanced prompt engineering.
Clinical imaging
Dec 6, 2024
This letter responds to the article "Encouragement vs. liability: How prompt engineering influences ChatGPT-4's radiology exam performance," offering additional perspectives on optimising ChatGPT-4 for Radiology applications. While the study highligh...
PhraseAug: An Augmented Medical Report Generation Model With Phrasebook.
IEEE transactions on medical imaging
Dec 2, 2024
Medical report generation is a valuable and challenging task, which automatically generates accurate and fluent diagnostic reports for medical images, reducing workload of radiologists and improving efficiency of disease diagnosis. Fine-grained align...
The added value of including thyroid nodule features into large language models for automatic ACR TI-RADS classification based on ultrasound reports.
Japanese journal of radiology
Nov 25, 2024
OBJECTIVE: The ACR Thyroid Imaging, Reporting, and Data System (TI-RADS) uses a score based on ultrasound (US) imaging to stratify the risk of nodule malignancy and recommend appropriate follow-up. This study aims to analyze US reports and explore ho...
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