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.

What has AI done for healthcare administration lately?
Administrative AI can meaningfully cut providers’ price tags on prior authorization processes. Yet the technology is not doing much to reduce overall system-level costs. And it tends to increase billing intensity, inflating medical spending as it does so.
Opportunistic AI detects colorectal cancer using routine, noncontrast CT
The COlorectal Cancer detection with AI, or COCA, model is a cost-effective, scalable solution that turns routine CT scans into opportunistic exams that can be used to proactively identify CRC.
AI not 'economically viable' if it doesn't replace at least some radiologists, experts claim
“Ignoring the economic drivers of automation will not prevent workforce transformation; it will only reduce our ability to shape it."
Users on a Discord chat are playing with Mythos, an AI deemed too dangerous for the public
Developer Anthropic released the tool to a small number of organizations in hopes they can find security vulnerabilities. Now comes a report revealing that a group of people on the popular Discord chat app have gained access. Mythos could be used as a powerful cybersecurity weapon.
New study offers insight into the type of AI radiologists prefer
Members of the specialty are more likely to trust domain-specific models when it comes to report generation support, research suggests.

What has AI done for healthcare administration lately?

Administrative AI can meaningfully cut providers’ price tags on prior authorization processes. Yet the technology is not doing much to reduce overall system-level costs. And it tends to increase billing intensity, inflating medical spending as it does so.

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Opportunistic AI detects colorectal cancer using routine, noncontrast CT

The COlorectal Cancer detection with AI, or COCA, model is a cost-effective, scalable solution that turns routine CT scans into opportunistic exams that can be used to proactively identify CRC.

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AI not 'economically viable' if it doesn't replace at least some radiologists, experts claim

“Ignoring the economic drivers of automation will not prevent workforce transformation; it will only reduce our ability to shape it."

artificial intelligence AI in healthcare

Users on a Discord chat are playing with Mythos, an AI deemed too dangerous for the public

Developer Anthropic released the tool to a small number of organizations in hopes they can find security vulnerabilities. Now comes a report revealing that a group of people on the popular Discord chat app have gained access. Mythos could be used as a powerful cybersecurity weapon.

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New study offers insight into the type of AI radiologists prefer

Members of the specialty are more likely to trust domain-specific models when it comes to report generation support, research suggests.

An ‘AI-native’—and lavishly funded—medical center grows in Texas

The largest single component of the overall project is likely to be the high-tech, high-touch hospital that’s now slated for opening in 2030.

GE HealthCare touts international expansion of its partnership with RadNet

The companies say they’re entering the next phase of collaboration, offering GEHC mammography systems paired with AI offerings from DeepHealth.

Healthcare AI often arrives with great expectations only to disappoint end-users as well as execs. Why is that?

By and large, U.S. hospitals and health systems do not have an AI adoption problem. But many—if not most—have an execution problem: They struggle to turn a set of installed AI tools into a measurable operational value.