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.

New research offers reminder of why ChatGPT should not be used for second opinions
Although these tools have proven themselves valuable in numerous settings, they must be used with caution, especially by patients and nonradiologist providers who may be seeking clarification on imaging reports.
GPT-4 confidently struggles on radiology exam
Image-based questions were significantly more challenging for the large language model to answer, despite the latest version now being capable of accepting image prompts.
AI improves CT assessments, boosts care for real-world heart patients
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.
Natural language processing spots reporting gaps, racial bias
Finding such discrepancies is critical to the continuity of patient care, as medical records and reports are often utilized across multiple providers and facilities.
ChatGPT shows 'significant promise' in guiding contrast-related decisions
This could be especially helpful when timely clinical decisions relative to the use of a contrast agent need to be made.

Addressing 'model drift' to recover AI performance before it leads to report errors

“Although regularly assessing and updating these models is necessary to ensure accurate performance, there is no standard approach to addressing model drift.”

August 16, 2022

Example of an artificial intelligence (AI) app store on the Sectra website, where Sectra PACS users can select the AI algorithms they want that are already integrated into the Sectra System. Other vendors have followed a similar approach to AI developed by many smaller vendors they partner with.

VIDEO: Development of AI app stores to enable easier access

Keith J. Dreyer, DO, PhD, FACR, American College of Radiology (ACR) Data Science Institute Chief Science Officer, explains how radiology vendors have developed AI app stores to make it easier to access new FDA cleared AI algorithms.

August 16, 2022

Keith J. Dreyer, DO, PhD, FACR, American College of Radiology (ACR) Data Science Institute Chief Science Officer, explains artificial intelligence (AI) for radiology. Dreyer also holds the positions of vice chairman of radiology at Massachusetts General Hospital, chief data science and information officer for the departments of radiology for both Massachusetts General Hospital and Brigham and Women's Hospital.

VIDEO: Where will radiology AI be in 5 years?

Keith J. Dreyer, DO, PhD, FACR, American College of Radiology (ACR) Data Science Institute Chief Science Officer, explains 5 developments to watch for in radiology artificial intelligence (AI).

August 16, 2022

How do radiologists really feel about adopting AI? New data offer insight

Up to 60% of radiologists have intentions of adopting artificial intelligence tools into clinical practice in the near future.

August 15, 2022

Educational platform releases AI course geared toward radiology administrators, technologists

The beginner course includes information on AI terminology, information technology principles, medical and legal considerations that accompany AI implementation and HIPAA compliance pertaining to the technology’s use in clinical settings.

August 10, 2022

Deep learning, subtraction technique ideal for evaluating stent re-stenosis on coronary CTA

Detecting in-stent restenosis via coronary CTA with hybrid iterative reconstruction has historically been an exercise in avoiding false positives.

August 10, 2022

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Less experienced radiologists benefit from deep learning models when scouting for intracranial aneurysms

Deep learning models can increase reader accuracy while simultaneously decreasing interpretation times when evaluating imaging for intracranial aneurysms.

August 8, 2022

Fat attenuation index (FAI) CT imaging of coronary artery fat can show inflammation and can help pin-point vulnerable plaques, or show the reversal of inflammation due to drug therapies. The technology is being developed by the vendor Caristo, which has European CE mark and the company is seeking FDA clearance. #SCCT #SCCT2022

VIDEO: New Technologies in Cardiac CT Imaging

Ron Blankstein, MD, Brigham and Women's Hospital, explains recent advances in coronary computed tomography angiography (CCTA) technology.

August 3, 2022

Around the web

Cardiovascular Business

AI improves CT assessments, boosts care for real-world heart patients

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.

Cardiovascular Business

AI predicts cardiovascular risk during CT scans—no invasive tests or contrast required

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

Radiology Business

Google teams with Bayer to address radiologist burnout via AI

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