BACKGROUND: Errors in radiology reports can result in inappropriate/harmful decisions. We investigated whether large language models can reduce the error rate.
BACKGROUND/OBJECTIVES: Reviewing the entire history of imaging exams of a single patient's records is an essential step in clinical practice, but it is time and resource consuming, with potential negative effects on workflow and on the quality of med...
PURPOSE: Prostate imaging reporting and data systems (PI-RADS) experiences considerable variability in inter-reader performance. Artificial Intelligence (AI) algorithms were suggested to provide comparable performance to PI-RADS for assessing prostat...
IEEE transactions on pattern analysis and machine intelligence
Apr 8, 2025
Given radiology images, automatic radiology report generation aims to produce informative text that reports diseases. It can benefit current clinical practice in diagnostic radiology. Existing methods typically rely on large-scale medical datasets an...
BACKGROUND: Labeling unstructured radiology reports is crucial for creating structured datasets that facilitate downstream tasks, such as training large-scale medical imaging models. Current approaches typically rely on Bidirectional Encoder Represen...
BACKGROUND: The application of artificial intelligence (AI) in the field of automatic imaging report labeling faces the challenge of manually labeling large datasets.
As artificial intelligence and digital medicine increasingly permeate healthcare systems, robust governance frameworks are essential to ensure ethical, secure, and effective implementation. In this context, medical image retrieval becomes a critical ...
Automated radiology reporting holds immense clinical potential in alleviating the burdensome workload of radiologists and mitigating diagnostic bias. Recently, retrieval-based report generation methods have garnered increasing attention. These method...
Diagnostic and interventional radiology (Ankara, Turkey)
Feb 28, 2025
PURPOSE: The primary objective of this research is to enhance the accuracy and efficiency of information extraction from radiology reports. In addressing this objective, the study aims to develop and evaluate a deep learning framework for named entit...
Journal of magnetic resonance imaging : JMRI
Feb 21, 2025
The American College of Radiology Liver Imaging Reporting and Data System (LI-RADS) is the preeminent framework for classification and risk stratification of liver observations on imaging in patients at high risk for hepatocellular carcinoma. In this...
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