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. 

ChatGPT large language models radiology health care

Latest version of ChatGPT has potential as a clinical decision support tool

Large language models could have feasibility in the future as clinical support tools that triage patients for imaging services—with additional updates and more training, of course.

June 22, 2023
Artificial intelligence automated measurements on an echocardiogram on the Siemens SyngoDynamics cardiovascular imaging and information solution. AI is helping speed workflows and complete tedious tasks faster and more accurately that humans, allowing sonographers and cardiologists to be more efficient. Photo by Dave Fornell

AI technologies to be featured heavily at ASE 2023

Artificial intelligence will be one of the hottest topics at the upcoming American Society of Echocardiography meeting in National Harbor, Maryland. 

June 21, 2023
artificial intelligence robot evaluates healthcare data. Novo Nordisk announced a new collaboration with Valo Health, a healthcare technology company focused on using artificial intelligence (AI) technology to identify new drug treatments for cardiovascular disease (CVD).

Many medical students believe AI poses a threat to the radiology job market

While radiologists and current trainees are either prepared to or are already embracing artificial intelligence, a significant portion of medical students shy away from radiology because of AI.

June 21, 2023
A figure from the study shows a chest radiograph with an area of consolidation involving right lower lung consistent with pneumonia, as well as right pleural effusion. The deep-learning model predicted risk of 30-day mortality of 9%. Right: Gradient- weighted class activation map shows that model prediction was influenced by separate area of image corresponding with heart and liver (yellow and light blue colors). Patient’s CURB-65 score was 4. Patient recovered from pneumonia and remained alive. AJR Image

Deep learning predicts pneumonia mortality on chest X-rays

AI was able to predict 30-day mortality risk predictions more accurately that the current risk assessment.

June 19, 2023
ChatGPT large language AI radiology patient information

Society of Interventional Radiology bests ChatGPT at informing patients—but contest reveals shortcomings on both sides

As a source of patient information, human-authored SIRweb.org beats ChatGPT on readability and, in a word, helpfulness. However, the website needs work on those scores too.

June 19, 2023
Brave Child

AI primed for pulmonary nodule detection in adults, falls short in pediatric population

Artificial intelligence tools have proven to be beneficial in detecting pulmonary nodules on chest CTs of adults, but less is known about their utility in pediatric populations. 

June 14, 2023
ChatGPT chatbot

ChatGPT effectively simplifies radiology reports, presents 'real opportunity' to better inform patients

Radiology reports are typically written in language well above the average American adult’s eighth grade reading level, making them a source of confusion for patients.

June 8, 2023
Virtual Reality

Virtual reality hypnosis could alter pain management during interventional procedures

The lead-up to radiation therapy can be anxiety-inducing and painful, but experts are optimistic a new relaxation method that is equal parts ancient and futuristic could help to ease patients’ worries. 

June 5, 2023

Around the web

Automated AI-generated measurements combined with annotated CT images can improve treatment planning and help referring physicians and patients better understand their disease, explained Sarah Jane Rinehart, MD, director of cardiac imaging with Charleston Area Medical Center.

Two advanced algorithms—one for CAC scores and another for segmenting cardiac chamber volumes—outperformed radiologists when assessing low-dose chest CT scans. 

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

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