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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Peter Kecskemethy PhD CEO co-founder Kheiron Medical

RadNet subsidiary DeepHealth acquires another artificial intelligence firm

The outpatient imaging center operator is adding Kheiron Medical Technologies, which uses deep learning to help radiologists detect breast cancer. 

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AI-based software improves follow-up imaging adherence by 74%

The increased follow-up of recommended imaging exams combined with the efficiency of its use also generated an additional $9,000 in revenue per month at one organization.

ChatGPT neurology interview with neurologist

Assessing whether ChatGPT would steer patients to an interventional radiologist

Icahn School of Medicine researchers recently asked the popular AI chatbot a series of questions related to ailments often treated by IR specialists. 

HeartFlow Plaque Analysis Example

New Category I CPT code issued for AI-enabled coronary plaque analysis software

The news comes just days after CMS confirmed these technologies would start receiving expanded Medicare coverage in November. HeartFlow and other AI vendors have already shared their excitement over the decision. 

radiology clock alarm turnaround time efficiency

Hands-free, generative AI system reduces radiologists' time spent creating reports

"Application of these technologies represents potential areas to combat burnout," researchers wrote recently in the Journal of the American College of Radiology. 

Example of an automated artificial intelligence (AI) assessment of soft coronary plaque from a CT scan from the vendor Cleerly. The AI gives a very detailed report of all the plaque in all the coronary vessels. Some cardiology experts believe this may be the way of the future in screening patients for early coronary disease and monitoring the impact of prevention efforts such as statins to determine if more aggressive treatments are needed.

Medicare administrative contractors approve coverage of AI-enabled quantitative CT

Four of the seven Medicare Administrative Contractors (MACs) announced they will now cover artificial intelligence-enabled quantitative coronary tomography (AI-QCT) and coronary plaque analysis (AI-CPA). 

Ron Blankstein, MD, Brigham and Womens Hospital, explains a study using AI opportunistic screening in non-cardiac CT scans looking for coronary artery disease.

Use of AI opportunistic screening in CT for cardiovascular disease

Ron Blankstein, MD, professor of radiology, Harvard Medical School, explains the use of artificial intelligence to detect heart disease in non-cardiac CT exams.

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American College of Radiology partners with FDA to hasten availability of AI products

The administration is seeking greater communication between the manufacturers of breakthrough devices and physician professional societies such as the ACR. 

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Around the web

Health Imaging
American College of Radiology announces significant expansion of Lung Cancer Screening Registry

The ACR hopes these changes, including the addition of diagnostic performance feedback, will help reduce the number of patients with incidental nodules lost to follow-up each year.

Health Imaging
AI reduces CT lung cancer screening workload by nearly 80%

And it can do so with almost 100% accuracy as a first reader, according to a new large-scale analysis.

Health Imaging
Nearly $800,000 settlement offered to family of man who died during MRI exam

The patient, who was being cared for in the ICU, was not accompanied or monitored by nursing staff during his exam, despite being sedated.