Artificial Intelligence

Artificial intelligence (AI) is becoming a crucial component of healthcare to help augment physicians and make them more efficient. In medical imaging, it is helping radiologists more efficiently manage PACS worklists, enable structured reporting, auto detect injuries and diseases, and to pull in relevant prior exams and patient data. In cardiology, AI is helping automate tasks and measurements on imaging and in reporting systems, guides novice echo users to improve imaging and accuracy, and can risk stratify patients. AI includes deep learning algorithms, machine learning, computer-aided detection (CAD) systems, and convolutional neural networks. 

FDA move signals a forthcoming increase in the use of virtual and augmented reality devices in radiology

The applications for VR/AR devices are wide-ranging, and could be particularly beneficial in underserved areas where patients have less access to care and clinicians have fewer opportunities to train.

December 12, 2022
Example of AI automated detection and highlighting of critical lung findings on a chest X-ray for a possible lung cancer nodule and fibrosis. Example shown by AI vendor Lunit.

VIDEO: Radiology AI trends at RSNA 2022

Sanjay Parekh, PhD, senior market analyst with Signify Research, discusses trends in radiology AI seen on the expo floor and in sessions at RSNA 2022.

December 12, 2022
Tablet projecting metaphorical medical hologram

AI-powered platform for heart failure detection gains FDA clearance

According to data submitted to the FDA, the platform has been linked to an accuracy of 90%, sensitivity of 87.8% and specificity of 83%.

December 8, 2022
AI is still one of the key technologies on the floor that many radiologists want to learn more about. A product rep discussing breast automated detection AI in the crowded Lunit booth at RSNA 2022.

9 technology trends and takeaways from RSNA 2022

Here are some interesting new technologies and key trends from the vast expo floor at the Radiological Society of North America 2022 meeting.

December 6, 2022
Jorge Soto, MD, chair of the RSNA Annual Meeting Program Planning Committee, chief of radiology, Boston Medical Center, and professor of medicine, Boston University School of Medicine, offers an overview of the trends, hot topics, and innovative research and technology at the Radiological Society of North America (RSNA) 2022 meeting.

VIDEO: Key takeaways from RSNA 2022

Jorge Soto, MD, chair of the RSNA Annual Meeting Program Planning Committee, chief of radiology, Boston Medical Center, offers an overview of the trends, hot topics, research and technology at the Radiological Society of North America (RSNA) 2022 meeting.

December 5, 2022
Dynamic lung air flow analysis just using X-ray without any contrast with new technology from 4D Medical.

PHOTO GALLERY: New technology and trends at RSNA 2022

Images from the Radiological Society of North America (RSNA) 2022 annual meeting Nov. 27- Dec. 1 in Chicago. The gallery includes new technologies and a look at sights around the world's largest radiology conference. 

December 1, 2022

Return-to-play protocols: Expert predicts radiologists will be future leaders in sports medicine

Several mainstream media outlets recently cast a bright spotlight upon the well-being of athletes who are returning to active rosters following injuries, causing many spectators to question the people in charge of making return-to-play decisions. 

December 1, 2022
An example of a lung cancer found using low-dose computed tomography (LDCT). Image courtesy of RSNA

New partnership seeks to streamline AI integration into lung cancer screening

A new partnership between Sirona Medical and RevealDX could streamline the process of AI integration into the clinical practice of lung nodule assessments. 

November 22, 2022

Around the web

"Gen AI can help tackle repetitive tasks and provide insights into massive datasets, saving valuable time," Thomas Kurian, CEO of Google Cloud, said Tuesday. 

SCAI and four other major healthcare organizations signed a joint letter in support of intravascular ultrasound. 

The newly approved AI models are designed to improve the detection of pulmonary embolisms and strokes in patients who undergo CT scans.

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