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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Why AI will make human knowledge more valuable

A recent editorial in STAT argued that as artificial intelligence (AI) continues to proliferate, the need for human providers will not decrease—rather, their knowledge will become more valuable in decision-making.

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Combination of AI, 3D printing software produces detailed organ models

Harvard Medical School researchers in collaboration with 3D bioprinting firm Aether recently introduced a 3D printing software that uses artificial intelligence (AI) to reproduce medical images of organs as 3D models.

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AI has made an impact—but its revolution may not be imminent

Artificial intelligence (AI) continues to change the way radiologists work. The major shift predicted by many isn’t happening as quickly as expected—but AI is reaching areas some didn’t anticipate.

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Study proposes ‘research framework’ for Alzheimer's based on biomarkers, not symptoms

Current Alzheimer’s disease research is primarily focused on symptoms, but a recent study proposed a new framework for understanding the disease based on biological brain changes and biomarkers.

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What healthcare can learn from Facebook's data scandal

Facebook's most recent data scandal had lawmakers grilling founder and CEO Mark Zuckerberg in a Senate hearing April 11 and presents bioethics lessons for healthcare leaders who are creating AI models for clinical decision making

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An expert's take on the future of machine learning in quantitative image analysis

W. Art Chaovalitwongse, PhD, from the University of Arkansas, discussed using radiomics versus deep learning-based features to predict clinical outcomes from medical imaging data.

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FDA approves primary care AI imaging device to detect eye disease in diabetics

Primary care physicians may now be able to identify moderate to severe levels of retinopathy in adult patients with diabetes using a recently FDA-approved artificial intelligence (AI) imaging device.

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New imaging technique detects prostate cancer not shown by MRI

Contrast-enhanced subharmonic imaging (SHI)—a new technique for imaging of microbubble ultrasound contrast agents—detected prostate cancers not identified by traditional MRI, according to a recent study presented at the American Roentgen Ray Society (ARRS) 2018 Annual Meeting.

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GE HealthCare launches new cardiac CT scanner with advanced AI capabilities

GE HealthCare designed the new-look Revolution Vibe CT scanner to help hospitals and health systems embrace CCTA and improve overall efficiency.

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Bracco updates HeartSee coronary flow capacity software with new diagnostic features

Clinicians have been using HeartSee to diagnose and treat coronary artery disease since the technology first debuted back in 2018. These latest updates, set to roll out to existing users, are designed to improve diagnostic performance and user access.

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Key trends in diagnostic heart testing: CT on the rise as some traditional techniques fall out of favor

The cardiac technologies clinicians use for CVD evaluations have changed significantly in recent years, according to a new analysis of CMS data. While some modalities are on the rise, others are being utilized much less than ever before.