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. 

From Capitol Hill to a hospital near you? 5 federal recommendations for healthcare AI policy

The 24 members of the House Task Force on AI—12 reps from each party—have posted a 253-page report detailing their bipartisan vision for encouraging innovation while minimizing risks. 

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Can large language models break language barriers in radiology reports?

With the growing demand for virtual care and an increasingly mobile population, the need to improve communication with non-English-speaking patients is immense. 

AI in healthcare

Most patients want to know if AI is involved in their care

“With this signal about the public’s preference for notification, the question for health systems and policymakers is not whether to notify patients but when and how.” 

Ischemic stroke shown in CT scans. Image courtesy of RSNA

New algorithm is twice as accurate at predicting stroke timing compared to the standard of care

Determining stroke onset is critical for management, as there is a small window of time for initiating treatment that can inhibit damage.

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Industry Watcher’s Digest

Will the second Trump Administration manage AI’s risks—or ‘unshackle’ AI’s potential? 

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Healthcare AI regulation needs nuance, balance: Research review

When regulating AI-equipped medical devices, the FDA might take a page from the Department of Transportation’s playbook for overseeing AI-equipped vehicles. These run the gamut from assisting human drivers to fully taking the wheel. 

Kate Hanneman, MD, chair of the Radiological Society of North America (RSNA) program planning committee, explains some of the key trends she saw in sessions during RSNA 2024. #RSNA #RSNA24 #RSNA2024

RSNA 2024 Program Chair Kate Hanneman highlights key trends in radiology

The cardiac radiologist and associate professor at the University of Toronto offered insights into key themes from the conference. 

How AI 'cheating' could impact algorithm reliability

A new study on the implications of AI shortcutting has experts raising concerns about the integration of the technology into medicine.