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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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.

Ed Nicol, MD, consultant cardiologist and honorary senior clinical lecturer with Kings College London and president-elect of the Society of Cardiovascular Computed Tomography (SCCT), explained artificial intelligence (AI) in cardiac CT is here to stay and its use is expanding. He noted that one AI-based algorithm is already included in recent cardiology guidelines and more will likely follow. #SCCT

Cardiac imagers need to understand AI as it enters clinical use and ACC guidelines

Most FDA-cleared AI algorithms are related to radiology and cardiology, meaning radiologists and cardiologists need to make an effort to learn how these technologies work.

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New AI healthcare challenge offers cash for better outcomes

The Applied AI Healthcare Challenge is looking for diverse and practical solutions that will help federal agencies provide high-quality care.

GE HealthCare acquires AI ultrasound company

The deal will fold Caption Health into GE HealthCare’s $3 billion ultrasound business, supporting the portfolio with AI-enabled imaging.

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Natural language processing generates CXR captions comparable to those from radiologists

Recent developments in NLP technology have improved its ability to recognize semantics and context, making it more likely that NLP could generate coherent medical reports without radiologist assistance. 

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GE Healthcare to acquire AI imaging specialists Caption Health

Caption Health has been a major player in the cardiac imaging space in recent years, gaining attention for its AI-powered echocardiography solutions. 

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