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. 

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An example of an FDA cleared radiology AI algorithm to automatically take a cardiac CT scan and identify, contour and quantify soft plaque in the coronary arteries. The Cleerly software then generates an automated report with images, measurements and a risk assessment for the patient. This type of quantification is too time consuming and complex for human readers to bother with, but AI assisted reports like this may become a new normal over the next decade. Example from Cleerly Imaging at SCCT 2022.

Legal considerations for artificial intelligence in radiology and cardiology

There are now more than 520 FDA-cleared AI algorithms and the majority are for radiology and cardiology, raising the question of who is liable if the AI gets something wrong.

Brent Savoie, MD, JD, vice chair for radiology informatics, section chief of cardiovascular imaging, Vanderbilt University, explains who will get sued when there is a misdiagnosis due to artificial intelligence (AI).

VIDEO: Who gets sued when radiology AI fails?

Brent Savoie, MD, JD, vice chair for radiology informatics, section chief of cardiovascular imaging, Vanderbilt University, explains who will get sued when there is a misdiagnosis due to artificial intelligence (AI).

FDA greenlights ultrasound MSK software with AI

Clarius Mobile Health of Vancouver, B.C., has won FDA approval to market an AI model that works with the company’s handheld point-of-care ultrasound devices to identify and measure tendons of the foot, ankle and knee.

AI helps reading-room radiologists differentiate colon cancer from diverticulitis

The model augmented and significantly improved diagnostic performance for abdominal subspecialists as well as residents—a result researchers say has major clinical implications.

Radiology residents appreciate, benefit by in-house AI training; attendings hungry too but may lack nonclinical time

Radiology residents who completed an intensive, single-day workshop in artificial intelligence came away reporting significantly improved understanding of the technology.

5 recent developments in thoracic imaging and what they may portend for radiology at large

Recent years have seen the venerable chest X-ray built upon with new technologies, screening programs and educational techniques. As a result, today’s thoracic imaging may be a humble herald of things to come across radiology.  

Example of artificial intelligence generated measurements to quantify the size of a lung cancer nodule during a followup CT scan to see if the lesion is regressing with treatment. This type of automation can aid radiologists by doing the tedious, time consuming work. Photo by Dave Fornell

8 trends in radiology technology to watch in 2023

Here is a list of some key trends in radiology technology from our editors based on our coverage of the radiology market.

Deep learning slashes real-world MRI scan times

Accelerated MRI with AI image reconstruction nearly halved orthopedic scan times while maintaining or even improving image quality in a newly published prospective study. 

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