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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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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GE Healthcare, Fujitsu Australia announce new AI partnership

GE Healthcare and Fujitsu Australia have announced a new collaboration focused on diagnosing and monitoring brain aneurysms using AI.

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How AI can improve breast cancer diagnoses

Researchers have developed a new machine learning system that can help pathologists make more accurate breast cancer diagnoses, sharing their findings in JAMA Network Open.

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What Has Artificial Intelligence Done for Radiology Lately?

RBJ sought out a representative sampling of radiologists who are not just talking about AI but already using it to beneficial effect. Here are five highlights from what we found.

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Robotic pal brings more than just fun to young hospital patients

A robotic animal companion has been making the rounds at the University of Nebraska Medical Center, where a pediatric patient in intensive care is recovering from multiple organ transplants.

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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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How AI could reduce unnecessary CT imaging for suspected PE

Machine learning models could help some patients with suspected pulmonary embolism (PE) avoid unnecessary CT imaging, according to new findings 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.