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.

AI differentiates 2 types of autoimmune arthritis on CT

Computer scientists, rheumatologists and immunologists have pooled skill sets to develop a neural network that can distinguish between rheumatoid and psoriatic arthritis while also recognizing healthy joints with no arthritis at all.

Hello Heart raises $70M as digital therapeutics demand soars

Hello Heart, a digital therapeutics company that focuses on heart health, has raised $70 million in a Series D funding round.

Unclear ownership, uneven assessments hamper informed-consent efforts in IR

The interventional radiology practice of an academic medical center has identified four challenges to securing informed consent from patients or their medical decision-makers. 

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3 indications auguring well for the future of pediatric PET/MRI

In pediatric care settings, hybrid PET/MR imaging combines “exquisite soft-tissue information obtained by MR imaging with functional information provided by PET.”

Portable MRI detects sports injuries near the point of play

Applied physicists have developed a portable MRI system that can screen young tennis players for wrist injuries in a minivan or suchlike passenger vehicle. 

Cross-sectional imaging ordered downstream for just 15% of emergency POCUS patients

Using point-of-care ultrasound in emergency settings does not lead to overutilization of follow-up imaging with cross-sectional CT, MRI or additional ultrasounds. 

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ARRS 2022 discusses pitfalls of radiologist 'tunnel vision'

"Inattention blindness bias" causes radiologists to unintentionally overlook what could be considered an obvious or significant finding.

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Most imaging AI algorithms perform unimpressively in external validation exercises

Some 81% of the models—70 of 86 DL algorithms reported in 83 separate studies—diminished at least somewhat in diagnostic accuracy compared with their accuracy on internal datasets.