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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Can machine learning accurately interpret free-text CT exams?

Interpreting free-text radiology reports can be a challenge for machine learning, according to a new article published in the Journal of the American College of Radiology.

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Looking ahead: 4 predictions about the future of AI in radiology

As much as the relationship between artificial intelligence (AI) and radiology has already developed, it is still in its earliest stages. What will that relationship look like in a decade? Or in another 20 or 30 years?

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Could deep learning technology improve arterial spin labeling image quality?

As the influence of artificial intelligence continues to grow, researchers are finding more and more new ways to take advantage of convolutional neural networks (CNNs) in healthcare. According to a new study published in Radiology, using a CNN as a deep learning algorithm can help improve the overall quality of arterial spin labeling (ASL) image quality.

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New AI algorithm predicts how well deaf children will learn language

Researchers have created a new algorithm that uses brain scans to predict language ability in deaf children after they receive a cochlear implant, according to a study published in the Proceedings of the National Academy of Sciences.

AI in Healthcare Summit to explore applications in robotics, imaging, interoperability

The two-day AI in Healthcare Summit on Thursday and Friday, Jan. 18 and 19, at the Harvard Club in Boston offers an in-depth discussion on the current state of the healthcare AI industry, AI-driven advancements in medical imaging and diagnostics, surgical robotics, patient engagement, integration and interoperability opportunities and challenges, bringing the human aspect back to healthcare, legal considerations and AI’s role in value-based care.

vRad announces new patent for escalating radiology procedures through AI

vRad, a MEDNAX company, announced this week that it has secured a patent for using artificial intelligence (AI) to escalate high-priority radiology procedures.

Researchers use machine learning to detect fractures in plain radiographs

Machine learning using deep convolutional neural networks (CNNs) can be used to detect fractures in plain radiographs, according to a new study published in Clinical Radiology.

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Software using machine learning algorithms accurately audits radiologist compliance

Imaging groups throughout the United States have moved to standardized radiology reports in recent years, and it’s a trend that continues to pick up steam. One side effect of this change is that leaders must then perform long, labor-intensive manual audits of their team’s reports to confirm compliance. But what if groups could somehow perform an automated audit, making those pesky manual audits a thing of the past?

Around the web

After reviewing years of data from its clinic, one institution discovered that issues with implant data integrity frequently put patients at risk. 

Prior to the final proposal’s release, the American College of Radiology reached out to CMS to offer its recommendations on payment rates for five out of the six the new codes.

“Before these CPT codes there was no real acknowledgment of the additional burden borne by the providers who accepted these patients."

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