Latest AI and machine learning research in diagnostic radiology for healthcare professionals.
BACKGROUND: In medical imaging courses, due to the complexity of anatomical relationships, limited number of practical course hours and instructors, how to improve the teaching quality of practical skills and self-directed learning ability has always been a challenge for higher medical education. Artificial intelligence-assisted diagnostic (AISD) software based on volume data reconstruction (VDR) ...
Radiology has a number of characteristics that make it an especially suitable medical discipline for early artificial intelligence (AI) adoption. These include having a well-established digital workflow, standardized protocols for image storage, and numerous well-defined interpretive activities. The more than 200 commercial radiologic AI-based products recently approved by the Food and Drug Admini...
In recent years, the role of Artificial Intelligence (AI) in medical imaging has become increasingly prominent, with the majority of AI applications a...
Artificial Intelligence (AI) has emerged as a transformative force within medical imaging, making significant strides within emergency radiology. Pres...
In the realm of utilizing artificial intelligence (AI) for medical image analysis, the paradigm of 'signal-image-knowledge' has remained unchanged. Ho...
Robotic-assisted TKA (RATKA) is a rapidly emerging technique that has been shown to improve precision and accuracy in implant alignment in TKA. Roboti...
Uncertainty regarding the future of radiologists is largely driven by the emergence of artificial intelligence (AI). If AI succeeds, will radiologists...
PURPOSE: To determine how radiology, nuclear medicine, and medical imaging journals encourage and mandate the use of reporting guidelines for artifici...
Machine Learning models need large amounts of annotated data for training. In the field of medical imaging, labeled data is especially difficult to ob...
Despite the surge in artificial intelligence (AI) development for health care applications, particularly for medical imaging applications, there has b...
Federated learning (FL) is a distributed machine learning framework that is gaining traction in view of increasing health data privacy protection need...
"Garbage in, garbage out" summarises well the importance of high-quality data in machine learning and artificial intelligence. All data used to train ...
The medical community does not broadcast the problem, but there are many studies that have reinforced a serious issue with diagnostic errors. A recent...
With the rapid development of medical imaging technology and computer technology, the medical imaging artificial intelligence of computer-aided diagno...
BACKGROUND: Accurate diagnosis and early treatment are essential in the fight against lymphatic cancer. The application of artificial intelligence (AI...
Artificial Intelligence (AI) methods, particularly Deep Neural Networks (DNNs), have shown great promise in a range of medical imaging tasks. However,...
Patients with facial trauma may suffer from injuries such as broken bones, bleeding, swelling, bruising, lacerations, burns, and deformity in the face...
This review article highlights the potential of integrating photon-counting computed tomography (CT) and deep learning algorithms in medical imaging t...
Deep learning has become a powerful tool for solving inverse problems in electromagnetic medical imaging. However, contemporary deep-learning-based ap...
Medical image segmentation is a fundamental and critical step in many image-guided clinical approaches. Recent success of deep learning-based segmenta...