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The Rise of AI in Medical Devices: What It Means for Hiring and Innovation

MRI Network

The number of machine learning- and AI-powered medical devices approved by the U.S. In 2024, the FDA approved nearly 1,000 AI medical devices compared to just 221 in 2023. At the same time, there are concerns about whether the FDA has approved some medical devices too quickly.

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CMS to make proposed MRI safety codes official

Health Imaging

The new code family was created to "capture the physician work involving patients with implanted medical devices that require access to MR diagnostic procedures."

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Hyperfine secures CE marking for Swoop portable MRI system

AuntMinnie

Connecticut-based company Hyperfine has secured CE marking under the European Medical Device Regulation for its Swoop portable brain MRI system. The Swoop system reduces scan times, which speeds the diagnostic process in acute care settings, Hyperfine said.

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Intellectt, Inc. Acquires U.S. Electronics, Inc. to Bolster Medical Device Manufacturing and Staffing Expertise

Imaging Technology

USEI), a reputable medical device manufacturing company with over 25 years of experience announced it has been acquired by Intellectt, Inc., Founded in 1994, USEI is an ISO 13485-certified medical device manufacturing company specializing in medical monitors and accessories. Electronics, Inc. Intellectt, Inc.

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ACR responds to Congressional query on AI reimbursement

AuntMinnie

Food and Drug Administration (FDA)-authorized radiology-specific AI products intended for use in a variety of applications that use diagnostic imaging data, Thorwarth's letter noted for Congress. As of May 6, the ACR estimated that there are approximately 250 U.S. William Thorwarth Jr.,

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AI will transform early cancer detection, save lives in 2025

AuntMinnie

This nascent fields impact on radiology, in particular, has been nothing short of astonishing, ushering in a new era of diagnostic reliability and personalized cancer care. Researchers are also pairing AI models with specific targeting compounds to help improve diagnostic accuracy. Roland Rott of GE HealthCare.

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How imaging AI developers can avoid pitfalls when testing algorithms

AuntMinnie

Postoperative changes and medical devices – Previous reports suggest that AI algorithms “are not properly trained” to recognize postoperative changes and how they impact image interpretation, the authors noted. Also, medical devices such as catheters, implants, prosthetics, or pacemakers can impact AI’s performance.

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