Latest AI and machine learning research in diagnostic radiology for healthcare professionals.
Recent advances in deep learning models have transformed medical imaging analysis, particularly in radiology. This editorial outlines how uncertainty quantification through embedding-based approaches enhances diagnostic accuracy and reliability in hepatobiliary imaging, with a specific focus on oncological conditions and early detection of precancerous lesions. We explore modern architectures like...
The kidney plays a vital role in maintaining homeostasis, but lifestyle factors and diseases can lead to kidney failures. Early detection of kidney disease is crucial for effective intervention, often challenging due to unnoticeable symptoms in the initial stages. Computed tomography (CT) imaging aids specialists in detecting various kidney conditions. The research focuses on classifying CT images...
Contrastive Language-Image Pre-training (CLIP), a simple yet effective pre-training paradigm, successfully introduces text supervision to vision model...
As the use of artificial intelligence (AI) continues to grow in radiology, it has become clear that its real-world performance often differs from that...
The recent emergence of text-to-image generative artificial intelligence (AI) diffusion models such as DALL-E, Firefly, Stable Diffusion, and Midjourn...
Transfer learning, particularly fine-tuning models pretrained on photographic images to medical images, has proven indispensable for medical image ana...
In recent years, with the advancement of medical imaging technology, medical image segmentation has played a key role in assisting diagnosis and treat...
Acute respiratory distress syndrome (ARDS) is a severe organ dysfunction associated with significant mortality and morbidity among critically ill pati...
Psychiatric diseases are bringing heavy burdens for both individual health and social stability. The accurate and timely diagnosis of the diseases is ...
Automated extraction of actionable details of recommendations for additional imaging (RAIs) from radiology reports could facilitate tracking and time...
Large-scale Artificial General Intelligence (AGI) models, including Large Language Models (LLMs) such as ChatGPT/GPT-4, have achieved unprecedented su...
The high volume of emergency room patients often necessitates head CT examinations to rule out ischemic, hemorrhagic, or other organic pathologies. A ...
Early prediction of recurrence in high-grade glioma (HGG) is critical due to its aggressive nature and poor prognosis. Distinguishing true recurrence ...
Convolutional Neural Networks (CNNs) have achieved remarkable segmentation accuracy in medical image segmentation tasks. However, the Vision Transform...
Medical image segmentation is an important task in medical imaging, as it serves as the first step for clinical diagnosis and treatment planning. Whil...
With the increasing popularity of medical imaging and its expanding applications, posing significant challenges for radiologists. Radiologists need to...
Artificial intelligence (AI) is revolutionizing diagnostic imaging, enhancing precision, speed and efficiency. This study explored radiologists' perce...
With the rapid development of artificial intelligence technology, its applications in medical imaging have become increasingly extensive. This review ...
Japan leads OECD countries in medical imaging technology deployment but lacks open, large-scale medical imaging databases crucial for AI development. ...
BACKGROUND: As a result of the 21st Century Cures Act, radiology reports are immediately released to patients. However, these reports are often too co...