Latest AI and machine learning research in diagnostic radiology for healthcare professionals.
BACKGROUND: Artificial intelligence-enhanced imaging techniques have demonstrated promising diagnostic potential for carotid plaques, a key cardiovascular and cerebrovascular risk factor. However, previous studies did not systematically synthesize their diagnostic accuracy. OBJECTIVE: This study aimed to quantitatively explore the diagnostic efficacy of deep learning (DL) and radiomics for extracr...
BACKGROUND: Artificial intelligence (AI)-assisted endoscopy facilitates upper gastrointestinal lesion detection. Whether Helicobacter pylori (H. pylori) infection influences its diagnostic performance remains unclear. This study evaluated the effect of H. pylori infection on an AI model's accuracy for diagnosing gastric neoplasms. METHODS: A deep convolutional neural network-based AI system was ev...
INTRODUCTION AND AIMS: In recent years, artificial intelligence (AI) has emerged as a powerful tool in medical imaging and in the analysis of complex ...
BACKGROUND AND OBJECTIVE: The advancement of generative AI in medical imaging faces the trilemma of simultaneously achieving high fidelity and diversi...
PURPOSE: Collections of interesting cases are at the heart of radiology education, but efficient saving and sharing of cases has always been a challen...
OBJECTIVE: Although artificial intelligence-based computer-aided diagnosis (AI-CAD) is increasingly applied in screening mammography, its use in diagn...
OBJECTIVE: Radiology residents require timely, personalized feedback to develop accurate image analysis and reporting skills. Increasing clinical work...
OBJECTIVES: To systematically review the evidence on the cost-effectiveness of artificial intelligence (AI) interventions for diagnostic imaging in ra...
Radiology is experiencing rapid and interconnected change, including rising imaging volumes, expanding access demands, and the introduction of artific...
BACKGROUND: Radiology is at the center of the digital transformation of the healthcare system. As a highly digital field, radiology is well-suited for...
Cybersecurity threats to medical imaging systems and workflows are no longer confined to information technology departments; they directly affect inte...
OBJECTIVES: To evaluate GPT-4o's zero-shot ability to extract structured diagnostic labels (with uncertainty) from free-text radiology reports and to ...
BACKGROUND: Artificial intelligence (AI) especially deep learning (DL) has significantly revolutionized medical image analysis, which include dental d...
Artificial intelligence (AI) is rapidly transforming diagnostic imaging, raising important questions about its role as a collaborative tool or a poten...
CT-based fractional flow reserve (CT-FFR) is a promising noninvasive method for the functional assessment of coronary stenosis. It expands the diagnos...
BACKGROUND: Timely detection and monitoring of abdominal aortic aneurysms (AAAs) are necessary to prevent ruptures and decrease mortality. Artificial ...
The integration of artificial intelligence (AI) into cardiovascular imaging and radiology offers the potential to enhance diagnostic accuracy, streaml...
This study aims to identify common errors in head and neck CTA reports using GPT-4, ERNIE Bot, and SparkDesk, evaluating their potential for supportin...
The integration of artificial intelligence (AI) into clinical practice, particularly within radiology, nuclear medicine and radiation oncology, is tra...
Education for medical imaging technologists or radiographers in regional and rural areas often faces significant challenges due to limited financial, ...