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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Allen, Dreyer explain why radiologists must be involved in AI development

Radiologists must step up and get involved with the design and development of AI tools relevant to radiology, according to a new column published in the Journal of the American College of Radiology. By just taking a “wait and see” approach, specialists risk being left out of the conversation altogether.

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4 things imaging providers should consider when choosing an AI vendor

Forty-seven percent of healthcare organizations are either already using artificial intelligence (AI) to help with medical imaging or actively planning to use AI, according to a new report published by KLAS. And adoption is expected to escalate sooner than later.

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The magic of machine learning: Why radiologists will love convolutional neural networks

Convolutional neural networks (CNNs) can be trained to analyze visual imagery much easier than other artificial neural networks, making their capabilities especially important to the future of radiology, according to a new analysis published in Current Problems in Diagnostic Radiology.

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Adapting to AI: 4 key takeaways from a survey of attending radiologists, trainees

To gain a better understanding of how the industry perceives radiology’s maturing relationship with artificial intelligence, researchers surveyed attending radiologists and trainees at a single diagnostic radiology (DR) residency program in August 2017, publishing their findings in the Journal of the American College of Radiology.

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Machine learning algorithms could predict onset of 'popcorn lung' post-transplant

A combination of machine learning and quantitative computed tomography (CT) could predict the eventual onset of bronchiolitis obliterans syndrome (BOS), also known as popcorn lung, in patients receiving lung transplants, researchers reported in Academic Radiology this week.

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To become leaders in AI, radiologists must address a variety of challenges

Artificial intelligence (AI) is one of the biggest topics in healthcare today, and the authors of a recent analysis published in the Journal of the American College of Radiology wrote at length about radiology’s role in the development and implementation of these state-of-the-art technologies.

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FDA clears oncology imaging software that tracks potential cancers with AI, deep learning

Arterys, a San Francisco-based healthcare company focused on cloud-based medical imaging technology, announced Thursday, Feb. 15, that it has received FDA clearance for its Arterys Oncology AI suite.

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FDA clears software that analyzes stroke images, alerts specialists using AI

The FDA announced late Tuesday, Feb. 13, that it has approved marketing of Contact, a clinical decision support software that analyzes CT results and notifies providers of potential strokes. Contact was developed by Viz.ai, a San Francisco-based healthcare company that specializes in artificial intelligence (AI) and deep learning technologies.

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