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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AI in healthcare? Many patients seem unimpressed

60% of U.S. adults would be uncomfortable if their healthcare provider relied on AI for their medical care.

Researchers building ‘commonsense AI’ from baby’s mind up

The project may inform theories of human neurodevelopment as dynamically as it advances computer and data science.

The evolution of care: 3 key takeaways from a new survey of cardiologists, health leaders and CVD patients

The report, developed by Abbott, examined everything from AI to social determinants of health. One key finding was that patients grade their overall satisfaction with a physician or hospital based on much more than the effectiveness of their treatment. 

Q&A: Dr. William Brody reflects on a radiological life well lived

As a high-schooler, he rebuilt a hospital’s discarded X-ray machine to learn the science of crystallography using the principles of Bragg diffraction.

A team of cardiologists from Cleveland Clinic and Stanford University recently tested ChatGPT, the popular artificial intelligence (AI) model, to see if it could accurately answer questions about preventive cardiology and cardiovascular disease. The model performed well, only missing a handful of questions, and the researchers concluded that ChatGPT showed considerable potential. Cleveland Clinic cardiologist Ashish Sarraju, MD, was the lead author of that study. #ChartGPT

ChatGPT and cardiology: A close look at the strengths and weaknesses of AI chatbots

Ashish Sarraju, MD, a cardiologist with Cleveland Clinic, discussed his recent research on ChatGPT, its potential to change patient care and more. 

The integration of artificial intelligence (AI) into radiology PACS and enterprise imaging systems has become a big topic of discussion with IT vendors over the past couple years. This has become a bigger question from hospitals and radiology groups as there are now about 400 radiology related AI algorithms that have U.S. Food and Drug Administration (FDA) clearance. Amy Thompson, a senior analyst at Signify Research, is monitoring AI trends in radiology and discusses trends.

Trends in the adoption and integration of AI into radiology workflows

Amy Thompson, a senior analyst at Signify Research, explains why AI adoption has been slow in radiology, common barriers and trends in the market.

Amy Thompson, a senior analyst at Signify Research, explains what she is seeing in the market for radiology PACS. She said the biggest overall, strategic technology trends are wider adoption of enterprise imaging systems expanding beyond radiology to include other departments, migration to cloud data storage, and adoption of artificial intelligence. Components of these integrate into the 5 trends in radiology IT systems outlined below.

5 key trends in PACS and enterprise imaging from Signify Research

Signify Research explains several key trends in the evolution of radiology PACS and enterprise imaging systems, including adoption of artificial intelligence, streamlining workflow, implementing structured reporting and more.

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Radiomics can predict major cardiac events using CCTA images

A CCTA-based radiomics method was recently found to be more accurate in identifying potentially problematic plaques than conventional CCTA anatomical parameters alone.