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. 

artificial intelligence malpractice

Against malpractice for using clinical AI, the best defense is a good offense

If a clinician you care about counts on AI to help make medical decisions, remind them: Tort law principles hold that doing so means risking liability should a patient sue over harm done.

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. 

The imaging iodine contrast shortage is delaying procedures and causing rationing at hospitals. impact is it having on hospitals and the tough decisions that are being made to triage patients to determine if they will get a contrast CT scan or an interventional or surgical procedure requiring contrast. Photo by Dave Fornell

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.

Lars Svensson, MD, PhD

Q&A: Cleveland Clinic’s Lars Svensson previews AATS annual meeting

Svensson, a prominent voice in cardiothoracic surgery, said he has seen a rise in enthusiasm ahead of this year's meeting.

Advanced artificial intelligence (AI) models can evaluate cardiovascular risk in routine chest CT scans without contrast, according to new research published in Nature Communications.[1] In fact, the authors noted, the AI approach may be more effective at identifying issues than relying on guidance from radiologists.

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. 

artificial intelligence machine learning healthcare

Industry Watcher’s Digest

Buzzworthy developments of the past few days.

kaiser permanente nurses protest AI in healthcare

Overheard around the Kaiser nurses’ protest over AI in healthcare

Healthcare leaders around the U.S. might want to take notice of what’s going on in the streets of San Francisco this week.

Dave Walker explains how AI is helping improve the revenue cycle in radiology. #RBMA #RBMA24 #RBMA2024

Use of AI in radiology revenue cycle management

Dave Walker, senior director of revenue cycle, Radiology Associates of North Texas, explains how his practice uses artificial intelligence for revenue cycle management during the Radiology Business Management Association (RBMA) 2024 meeting.