Diagnostic Radiology

Automated Detection of Radiology Reports that Require Follow-up Imaging Using Natural Language Processing Feature Engineering and Machine Learning Classification.

While radiologists regularly issue follow-up recommendations, our preliminary research has shown that anywhere from 35 to 50% of patients who receive follow-up recommendations for findings of possible cancer on abdominopelvic imaging do not return for follow-up. As such, they remain at risk for adverse outcomes related to missed or delayed cancer diagnosis. In this study, we develop an algorithm t...

How the FDA Regulates AI.

Recent years have seen digital technologies increasingly leveraged to multiply conventional imaging modalities' diagnostic power. Artificial intelligence (AI) is most prominent among these in the radiology space, touted as the "stethoscope of the 21st century" for its potential to revolutionize diagnostic precision, provider workflow, and healthcare expenditure. Partially owing to AI's unique char...

An Intelligent Future for Medical Imaging: A Market Outlook on Artificial Intelligence for Medical Imaging.

Radiologists today are under increasing work pressure. We surveyed radiologists in the United States across practice settings, and the overwhelming ma...

Basics of Deep Learning: A Radiologist's Guide to Understanding Published Radiology Articles on Deep Learning.

Artificial intelligence has been applied to many industries, including medicine. Among the various techniques in artificial intelligence, deep learnin...

Deep Learning: A Breakthrough in Medical Imaging.

Deep learning has attracted great attention in the medical imaging community as a promising solution for automated, fast and accurate medical image an...

Strengths, Weaknesses, Opportunities, and Threats Analysis of Artificial Intelligence and Machine Learning Applications in Radiology.

Currently, the use of artificial intelligence (AI) in radiology, particularly machine learning (ML), has become a reality in clinical practice. Since ...

The Role of Generative Adversarial Networks in Radiation Reduction and Artifact Correction in Medical Imaging.

Adversarial networks were developed to complete powerful image-processing tasks on the basis of example images provided to train the networks. These n...

Artificial Intelligence and Clinical Decision Support for Radiologists and Referring Providers.

Recent advances in artificial intelligence (AI) are providing an opportunity to enhance existing clinical decision support (CDS) tools to improve pati...

Eye Tracking for Deep Learning Segmentation Using Convolutional Neural Networks.

Deep learning with convolutional neural networks (CNNs) has experienced tremendous growth in multiple healthcare applications and has been shown to ha...

RIL-Contour: a Medical Imaging Dataset Annotation Tool for and with Deep Learning.

Deep-learning algorithms typically fall within the domain of supervised artificial intelligence and are designed to "learn" from annotated data. Deep-...

Toward Complete Structured Information Extraction from Radiology Reports Using Machine Learning.

Unstructured and semi-structured radiology reports represent an underutilized trove of information for machine learning (ML)-based clinical informatic...

[Research progress in medical imaging based on deep learning of neural network].

The development of computer hardware allows rapid accumulation of medical imaging data. Deep learning has shown great potential in medical imaging dat...

The Artificial Intelligence-Enabled Medical Imaging: Today and Its Future.

Medical imaging is now being reshaped by artificial intelligence (AI) and progressing rapidly toward future. In this article, we review the recent pro...

Collaborations of Industry, Academia, Research and Application Improve the Healthy Development of Medical Imaging Artificial Intelligence Industry in China.

In recent years, artificial intelligence (AI) has developed rapidly in the field of medical imaging. However, the collaborations among hospitals, rese...

Artificial intelligence at the intersection of pathology and radiology in prostate cancer.

Pathologic grading plays a key role in prostate cancer risk stratification and treatment selection, traditionally assessed from systemic core needle b...

Integrating an Ontology of Radiology Differential Diagnosis with ICD-10-CM, RadLex, and SNOMED CT.

An ontology offers a human-readable and machine-computable representation of the concepts in a domain and the relationships among them. Mappings betwe...

Automated Diagnosis of Lymphoma with Digital Pathology Images Using Deep Learning.

Recent studies have shown promising results in using Deep Learning to detect malignancy in whole slide imaging, however, they were limited to just pre...

Integrating ontologies of human diseases, phenotypes, and radiological diagnosis.

Mappings between ontologies enable reuse and interoperability of biomedical knowledge. The Radiology Gamuts Ontology (RGO)-an ontology of 16 918 disea...

Artificial Intelligence and Radiology in Singapore: Championing a New Age of Augmented Imaging for Unsurpassed Patient Care.

Artificial intelligence (AI) has been positioned as being the most important recent advancement in radiology, if not the most potentially disruptive. ...

Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine.

The discipline of radiology and diagnostic imaging has evolved greatly in recent years. We have observed an exponential increase in the number of exam...