Health IT

Healthcare information (HIT) systems are designed to connect all the elements together for patient data, reports, medical imaging, billing, electronic medical record (EMR), hospital information system (HIS), PACS, cardiology information systems (CVIS)enterprise image systemsartificial intelligence (AI) applications, analytics, patient monitors, remote monitoring systems, inventory management, the hospital internet of things (IOT), cloud or onsite archive/storage, and cybersecurity.

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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. 

Sectra reinforces top standing among PACS suppliers recognized by KLAS; Fujifilm stays strong with VNA

Healthcare research outfit KLAS is out with its 2023 Best in KLAS awards recognizing excellence in healthcare software and services, and the Sweden-based imaging IT and cybersecurity company Sectra tops the field of U.S. PACS vendors—again.  

Which risk stratification system is best for classifying thyroid nodules?

A new analysis compared the results of 39 published studies and nearly 50,000 patient cases to rank the performances of six different thyroid nodule stratification systems.

Appearances can be deceiving on chest CT performed for COVID in cancer patients

In a study of more than 250 COVID-positive patients with a history of any cancer, fewer than half the cohort had chest CT findings deemed typical for COVID-related pneumonia based on an RSNA classification guide. 

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Amyloid plaque patterns on PET imaging predict Alzheimer's progression in asymptomatic patients

Experts involved in the new research suggest that identifying these spatiotemporal variations could play an important role in clinical research and precision medicine. 

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6 tips for integrating NPPs into imaging practice

The role of non-physician practitioners will inevitably continue to grow in healthcare, but how will their presence impact radiology?  

An example of artificial intelligence (AI) automated detection of a intracranial hemorrhage (ICH) in. a CT scan used to send alerts to the stroke acute care team before a radiologist even sees the exam. Example shown by TeraRecon at RSNA 2022.

FDA has now cleared more than 500 healthcare AI algorithms

More than 500 clinical AI algorithms have now been cleared by the FDA, with the majority just in the past couple years.

An example of an FDA cleared radiology AI algorithm to automatically take a cardiac CT scan and identify, contour and quantify soft plaque in the coronary arteries. The Cleerly software then generates an automated report with images, measurements and a risk assessment for the patient. This type of quantification is too time consuming and complex for human readers to bother with, but AI assisted reports like this may become a new normal over the next decade. Example from Cleerly Imaging at SCCT 2022.

Legal considerations for artificial intelligence in radiology and cardiology

There are now more than 520 FDA-cleared AI algorithms and the majority are for radiology and cardiology, raising the question of who is liable if the AI gets something wrong.