Diagnostic Radiology

Current challenges of implementing artificial intelligence in medical imaging.

The idea of using artificial intelligence (AI) in medical practice has gained vast interest due to its potential to revolutionise healthcare systems. However, only some AI algorithms are utilised due to systems' uncertainties, besides the never-ending list of ethical and legal concerns. This paper intends to provide an overview of current AI challenges in medical imaging with an ultimate aim to fo...

Use of artificial intelligence in emergency radiology: An overview of current applications, challenges, and opportunities.

The value of artificial intelligence (AI) in healthcare has become evident, especially in the field of medical imaging. The accelerated pace and acuity of care in the Emergency Department (ED) has made it a popular target for artificial intelligence-driven solutions. Software that helps better detect, report, and appropriately guide management can ensure high quality patient care while enabling em...

Surgical Selection of T1 Stage Renal Tumor Resection Based on Imaging MAP Score under Smart Medical Care.

Smart medical uses the medical information platform and the current technological means to enable the process of sharing information between medical s...

Incidentalomas in chest CT.

Advances in imaging technology have dramatically increased the resolution of CT and improved detection of disease; these advances also have led to an ...

Federated Learning in Medical Imaging: Part I: Toward Multicentral Health Care Ecosystems.

With recent developments in medical imaging facilities, extensive medical imaging data are produced every day. This increasing amount of data provides...

Federated Learning in Medical Imaging: Part II: Methods, Challenges, and Considerations.

Federated learning is a machine learning method that allows decentralized training of deep neural networks among multiple clients while preserving the...

U-Net-Based Medical Image Segmentation.

Deep learning has been extensively applied to segmentation in medical imaging. U-Net proposed in 2015 shows the advantages of accurate segmentation of...

Recent advances and clinical applications of deep learning in medical image analysis.

Deep learning has received extensive research interest in developing new medical image processing algorithms, and deep learning based models have been...

Non-radiologist perception of the use of artificial intelligence (AI) in diagnostic medical imaging reports.

INTRODUCTION: Incorporating artificial intelligence (AI) in diagnostic medical imaging reports has the potential to improve efficiency. Although perce...

Clinical language search algorithm from free-text: facilitating appropriate imaging.

BACKGROUND: The comprehensiveness and maintenance of the American College of Radiology (ACR) Appropriateness Criteria (AC) makes it a unique resource ...

Systematic Review of Generative Adversarial Networks (GANs) for Medical Image Classification and Segmentation.

In recent years, generative adversarial networks (GANs) have gained tremendous popularity for various imaging related tasks such as artificial image g...

Application of Artificial Intelligence in Cardiovascular Imaging.

During the last two decades, as computer technology has matured and business scenarios have diversified, the scale of application of computer systems ...

The concept of the invisible radiologist in the era of artificial intelligence.

The radiologists were traditionally working in the background. What upgraded them as physicians during the second half of the past century was their c...

Social Asymmetry, Artificial Intelligence and the Medical Imaging Landscape.

Social and health care equity and justice should be prioritized by the mantra of medicine, first do no harm. Despite highly motivated national and glo...

Domain generalization on medical imaging classification using episodic training with task augmentation.

Medical imaging datasets usually exhibit domain shift due to the variations of scanner vendors, imaging protocols, etc. This raises the concern about ...

AI in spotting high-risk characteristics of medical imaging and molecular pathology.

Medical imaging provides a comprehensive perspective and rich information for disease diagnosis. Combined with artificial intelligence technology, med...

Automatic Searching and Pruning of Deep Neural Networks for Medical Imaging Diagnostic.

The field of medical imaging diagnostic makes use of a modality of imaging tests, e.g., X-rays, ultrasounds, computed tomographies, and magnetic reson...

Construction and Evaluation of Intelligent Medical Diagnosis Model Based on Integrated Deep Neural Network.

In recent years, as human life expectancy increases, birth rate decreases and health management concerns; the traditional Healthcare imaging system, w...

An Open Medical Platform to Share Source Code and Various Pre-Trained Weights for Models to Use in Deep Learning Research.

Deep learning-based applications have great potential to enhance the quality of medical services. The power of deep learning depends on open databases...

Predicting cancer outcomes with radiomics and artificial intelligence in radiology.

The successful use of artificial intelligence (AI) for diagnostic purposes has prompted the application of AI-based cancer imaging analysis to address...