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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Traditional methods continue to outperform AI in some orthopedic scenarios

A new meta-analysis suggests that when it comes to hip fractures, AI algorithms do not always live up to their hype. 

Commercially available AI tool could reduce radiologist workloads by 10% or more

The tool’s sensitivity was recorded as 99.1% for abnormal radiographs and 99.8% for critical radiographs—better than two board-certified radiologists who also interpreted the exams. 

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Deep learning model predicts Alzheimer's using routine MRI exams

When put to the test, the new model was able to predict Alzheimer’s risk with 90.2% accuracy.

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AI competition furthers research on computer-aided detection in breast imaging

For the challenge, eight teams were tasked with developing algorithms capable of achieving high sensitivity for lesion detection on DBT exams.

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.

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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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