Medical Imaging

Physicians utilize medical imaging to see inside the body to diagnose and treat patients. This includes computed tomography (CT), magnetic resonance imaging (MRI), X-ray, ultrasound, fluoroscopy, angiography,  and the nuclear imaging modalities of PET and SPECT. 

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The European Society of radiology European Congress of Radiology (ECR) 2023 meeting. Image courtesy of ECR

Key trends in radiology at the European Congress of Radiology 2023 meeting

Bhvita Jani, research manager at the healthcare market analysis firm Signify Research, shares noteworthy happenings from the ECR expo floor.

Brain imaging artificial intelligence is a primary area of concentration for AI because oif the critical nature of fast detection and treatment for patients. This is an example of the AI applications displayed by third-party advanced visualization vendor TeraRecon at RSNA 2022.

What is the ROI on AI adoption in radiology?

Radiology makes up the vast majority of FDA-cleared AI algorithms, but with minimal or no reimbursement, hospital administrators may ask whether AI’s value justifies its expense.

artificial intelligence AI deep learning

Cardiologists find AI to be more accurate than sonographers at interpreting echocardiograms

Overall, cardiologists made corrections to 16.8% of AI-generated assessments and 27.2% of sonographer assessments.

Point of Care Ultrasound POCUS Lungs

Specialized lung AI tool cleared for U.S. sales

A popular marketer of handheld ultrasound devices has won FDA approval for an AI-enabled B-line quantification tool for use with patients suspected of having compromised lung function.

Emergency physician in COVID mask

Study supports feasibility of full-time AI-based workflow in the ED

When used to flag anomalies in combined chest and musculoskeletal X-rays, imaging AI can relieve overstretched emergency radiology teams.

Brainomix stroke CT imaging

Explainable stroke AI cleared for US sales

The FDA has OK’d a new artificial intelligence platform for quickly diagnosing stroke on unenhanced CT scans.

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New algorithm shows how AI could make lung cancer screening more cost-effective

Using CT scans, a deep learning algorithm can recommend optimal lung cancer screening intervals by distinguishing between high-risk and low-risk lung nodules. 

 

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Biological ‘brain age’ could help pave the way for more personalized medicine

AI-powered analysis can now assess cognitive decline by noting gaps in chronological versus biological “brain age.”

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