Latest AI and machine learning research in diagnostic radiology for healthcare professionals.
Deep learning-based vision models are playing an increasingly pivotal role in clinical diagnosis and treatment.However, existing approaches predominantly rely on visual information, often neglecting the accompanying radiology reports. Even when textual data is considered, current methods typically restrict their input to discrete text labels, failing to exploit the comprehensive, fine-grained sema...
BACKGROUND: Transformer-based architectures have rapidly gained prominence in medical imaging due to their ability to model long-range dependencies and global contextual information more effectively than convolutional neural networks. In dentistry, their applications have expanded across diagnostic, predictive, and generative tasks, yet no comprehensive synthesis has systematically evaluated their...
In recent years, with the development of medical imaging and deep learning technologies, medical image segmentation has played a crucial role in assis...
With the rapid growth of the use of computed tomography, advances in artificial intelligence enable opportunistic screening, the systematic extraction...
Opportunistic findings at imaging (iOFs), such as osteoporosis, liver steatosis, or coronary artery calcifications, are clinically relevant abnormalit...
BACKGROUND: Pulmonary edema is a life-threatening condition caused by fluid accumulation in the lungs that impairs gas exchange. Machine learning mode...
Deep learning-based medical image-to-mesh reconstruction has rapidly evolved, enabling the transformation of medical imaging data into three-dimension...
PURPOSE: Large language models (LLMs) are increasingly integrated into radiology workflows, but their demographic biases have not been evaluated in di...
BACKGROUND: Large language model (LLM) proofreaders for radiology reports generate many false positives (FPs) due to the low prevalence of errors. OBJ...
PURPOSE: To compare the diagnostic performance of apparent diffusion coefficient (ADC) map-based radiomics with conventional CT and DWI for differenti...
OBJECTIVES: This study aims to provide a comprehensive bibliometric mapping of the scientific evolution and research trends of fractal analysis (FA) i...
BACKGROUND: The use of artificial intelligence (AI) in medical imaging has been growing exponentially. Understanding patient perceptions and factors i...
BACKGROUND: Urine cytology is a noninvasive tool for detecting urothelial carcinoma, yet its performance depends heavily on expert cytologists and tim...
OBJECTIVE: Artificial intelligence (AI) is transforming medical imaging and radiation oncology, yet limited understanding and access to education hind...
BACKGROUND: Artificial intelligence (AI) is increasingly used in radiological diagnostics, particularly for screening, detection, and prioritization o...
Radiology strongly impacts patient care. However, radiologists' potential to promote high-value care has long been underappreciated, partly related to...
BACKGROUND: Artificial intelligence (AI) is increasingly recognized as a valuable tool for the early detection and prognosis of oral cancer, addressin...
Reliable and secure transmission of medical images is essential for telemedicine, remote diagnosis, and distributed healthcare systems. However, medic...
Informed consent in radiology is often constrained by limited time and patient preparation. The KIPA project combines digital consent forms with an AI...
Peer review is the result of a long historical evolution. From the Greek philosophy (the metaphor of Socratic maieutics) to the Cartesian method of sy...