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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MRI reveals ‘smoldering’ brain spots may indicate severe MS

“We hope these results will help test the effectiveness of new therapies for this form of MS and reduce the suffering patients experience,” said lead author of the study published in JAMA Neurology.

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Neuroimaging shows e-devices may hamper understanding of complex concepts

A recent fMRI-based study found that learning via electronic devices such as smartphones or tablets can inhibit the brain’s ability to understand scientific texts, according to a recent study published by Scientific Reports.

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ADHD medication can negatively impact child brain development

A commonly prescribed drug used to treat attention-deficit/hyperactivity disorder (ADHD) disproportionately affects the development of children’s brains compared to adults with ADHD, according to a new study published in Radiology.

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AI helps pathologists diagnose difficult breast cancer cases

A new machine learning system created by UCLA researchers may help doctors classify breast cancers that are notoriously difficult to diagnose, according to an Aug. 9 study published in JAMA Network Open.

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Machine learning can avoid unnecessary CT use in PE patients

A neural network model can scour electronic medical record (EMR) data and determine if a patient has imaging-specific pulmonary embolism (PE)—a potential remedy for unnecessary CT imaging, reported authors of a multicenter study published in JAMA Network Open.

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Ultrasound-guided IV placement improves first-attempt success in children

Physicians who used ultrasound to guide the placement of intravenous (IV) lines in young patients had better first-attempt success rates than those who used traditional methods, according to a study published in the July issue of Annals of Emergency Medicine.

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Deep learning triages mammograms, reduces radiologists' workload nearly 20%

Deep learning can be used to triage cancer-free mammograms and improve the efficiency of radiologists, according to an Aug. 6 feasibility study published in Radiology.

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Prepare for more AI at RSNA 2019

The 2019 Radiological Society of North America (RSNA) annual meeting in Chicago is expanding its artificial intelligence offerings.

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Positron partners with Upbeat Cardiology Solutions to improve cardiac PET/CT access

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