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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Top 10 trends to follow in diagnostic radiology

Consulting firm Vizient published its fifth annual imaging trends report, highlighting issues such as staffing, remote scanning, AI, sustainability and "systemness." 

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Industry Watcher’s Digest

Buzzworthy developments of the past few days.

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‘First federal CIO’ gives 3 reality checks on governmental AI

The White House’s Office of Management and Budget recently released final guidance for the use of AI by executive-branch departments and agencies.

deep learning algorithm slashes radiation dose during neck CT scans

Deep learning denoising software can cut radiation exposure to 25% of conventional doses during neck CT

And the dose reduction can be achieved without sacrificing image quality, a new analysis shows.

'Too many cooks in the kitchen': How AI is both helpful and a hindrance to radiologists

Recent research suggests that, in many cases, radiologist performance only improves minimally with AI assistance.

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Machine learning helps experts identify ADHD biomarkers on MRI scans

Experts have identified numerous white matter features, in addition to changes in white matter tracts that occur over time, which they believe are more common among people with ADHD.

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Age, race and density status influence AI performance on mammogram reads

Although studies have shown AI to be effective as a support tool, several have also highlighted issues related to the potential for bias in algorithms that have not been trained on diverse datasets. 

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Industry Watcher’s Digest

Buzzworthy developments of the past few days.

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